rm(list=ls()) # reset workspace
options(scipen=100) # do not show numbers using exponential
# install & load packages
pacman::p_load(
openxlsx # open excel
, tidyverse
, magrittr # extended pipe
, foreach # Parallel processing
, metafor # meta-analysis
, MCMCglmm # comparative analysis
, orchaRd # Orchard plot - meta-analysis
, rotl # open tree of life
, ape # Phylogeny
, phytools # Phylogeny
, knitr
, kableExtra # nice tables
, pander # nice tables
, svglite # Export SVG plots
, ggiraphExtra # Geom predict
, ggbeeswarm # Orchard plot
, ggtree
, patchwork # combine multiple plots
# , R.rsp
)
# Rmarkdown settings
knitr::opts_chunk$set(
prompt = FALSE, # Do not add > or + in inline-code
message = FALSE,
comment = "",
warning = FALSE, # Mute warnings
tidy = TRUE
)
options(knitr.kable.NA = '') # Hide NAs in kable table
We have custom functions named : get_pred(), get_reg(), get_fixed.MCMCglmm() and get_random.MCMCglmm(), all of which are used later (see below for their functionality) and the code are included here.
#' Title: get_pred
#' function to get prediction intervals (crediblity intervals) from rma objects (metafor)
#'
#' @param model: rma.mv object
#' @param mod: the name of a moderator
get_pred <- function(model, mod = " ") {
I2 <- i2_ml(model) %>%
as.data.frame() %>%
rownames_to_column("Partition") %>%
rename("I2" = ".") %>%
mutate(percentage = str_c(round(I2*100,1),"%", sep = "")) %>%
column_to_rownames("Partition")
name <- as.factor(str_replace(row.names(model$beta), mod, ""))
len <- length(name)
if (len != 1) {
newdata <- matrix(NA, ncol = len, nrow = len)
for (i in 1:len) {
# getting the position of unique case from X (design matrix)
pos <- which(model$X[, i] == 1)[[1]]
newdata[, i] <- model$X[pos, ]
}
pred <- predict.rma(model, newmods = newdata)
} else {
pred <- predict.rma(model)
}
table <- tibble(
'Dataset' = "",
'Fixed effects' = name,
'Estimate' = model$beta, # regression: estimate
'LowerCI' = model$ci.lb, # regression: 95ci,
'UpperCI' = model$ci.ub, # regression: 95ci
'lowerPR' = pred$cr.lb, # lower prediction range
'upperPR' = pred$cr.ub, # lower prediction range
'P' = model$pval,
# 'V[species with phylogeny]' =
# c(model$sigma2[1], rep("", length(model$beta))),
# 'V[study]' =
# c(model$sigma2[2], rep("", length(model$beta))),
# 'V[crossed strain]' =
# c(model$sigma2[3], rep("", length(model$beta))),
# 'V[residual]' =
# c(model$sigma2[4], rep("", length(model$beta))),
'I2[total]' =
c(I2["I2_total","percentage"], rep("", length(model$beta)-1)),
'%I2[species with phylogeny]' =
c(I2["I2_spL.name","percentage"], rep("", length(model$beta)-1)),
'%I2[study]' =
c(I2["I2_Study.ID","percentage"], rep("", length(model$beta)-1)),
'%I2[crossed strain]' =
c(I2["I2_Cross.ID","percentage"], rep("", length(model$beta)-1)),
'%I2[residual]' =
c(I2["I2_ES.ID","percentage"], rep("", length(model$beta)-1)),
) %>%
as.data.frame() %>%
mutate_at(vars('Estimate':'P'), as.numeric) %>%
mutate(significance = ifelse(P < 0.05, "*", ""))
}
#' Title: get_reg
#' function to get summary stats from rma objects (metafor)
#' @param model: rma.mv object
#' @param mod: the name of a moderator
get_reg <- function(model, mod = " ") {
I2 <- i2_ml(model) %>%
as.data.frame() %>%
rownames_to_column("Partition") %>%
rename("I2" = ".") %>%
mutate(percentage = str_c(round(I2*100,1),"%", sep = "")) %>%
column_to_rownames("Partition")
table <- tibble(
'Dataset' = "",
'Fixed effects' = c("", row.names(model$beta)),
'Estimate' = c("", model$beta), # regression: estimate
'LowerCI' = c("", model$ci.lb), # regression: 95ci,
'UpperCI' = c("", model$ci.ub), # regression: 95ci
'P' = c("", model$pval),
# 'V[species with phylogeny]' =
# c(model$sigma2[1], rep("", length(model$beta))),
# 'V[study]' =
# c(model$sigma2[2], rep("", length(model$beta))),
# 'V[crossed strain]' =
# c(model$sigma2[3], rep("", length(model$beta))),
# 'V[residual]' =
# c(model$sigma2[4], rep("", length(model$beta))),
'I2[total]' =
c(I2["I2_total","percentage"], rep("", length(model$beta))),
'%I2[species with phylogeny]' =
c(I2["I2_spL.name","percentage"], rep("", length(model$beta))),
'%I2[study]' =
c(I2["I2_Study.ID","percentage"], rep("", length(model$beta))),
'%I2[crossed strain]' =
c(I2["I2_Cross.ID","percentage"], rep("", length(model$beta))),
'%I2[residual]' =
c(I2["I2_ES.ID","percentage"], rep("", length(model$beta))),
) %>%
as.data.frame() %>%
mutate_at(vars('Estimate':'P'), as.numeric) %>%
mutate(significance = ifelse(P < 0.05, "*", ""))
}
#' Title: get_random.MCMCglmm
#'
#' @param x: VCV of MCMCglmm products (i.e. summary('MCMCglmm object'$VCV)), which was transformed if apprecable.
get_random.MCMCglmm <- function(x) {
bind_cols(
x$statistics %>%
as.data.frame,
x$quantiles %>%
as.data.frame
) %>%
rownames_to_column("Random effects") %>%
rename("Estimates" = "Mean") %>%
as.data.frame %>%
mutate(
'95% credible interval' = str_c(
round(.[,"2.5%"],2), round(.[,"97.5%"],2),
sep = "--" # en-dash
)
) %>%
dplyr::select("Random effects", "Estimates", "95% credible interval")
}
#' Title: get_fixed.MCMCglmm
#'
#' @param x: Solution of MCMCglmm products (i.e. summary('MCMCglmm object'$solutions)), which was transformed if apprecable.
get_fixed.MCMCglmm <- function(x) {
x %>%
as.data.frame() %>%
rownames_to_column(var = "Factors") %>%
mutate(
'95% credible interval' = str_c(
round(.[,"l-95% CI"],2), round(.[,"u-95% CI"],2),
sep = " -- " # en-dash
),
Estimates = round(post.mean, 2),
P = round(pMCMC, 3)
) %>%
mutate(significance = ifelse(P < 0.05, "*", "")) %>%
mutate(
"Description" =
str_c("$\\beta$ = ", Estimates,
", CI = ", .[, "95% credible interval"],
", P = ", round(P, 3),
"")
)
}
#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for forest plots
#++++++++++++++++++++++++++++++++++++++++++++++#
foresttheme <- theme_bw() +
theme(
legend.position = "none",
axis.line = element_line(size = 0.4, color = "grey50"),
axis.ticks = element_line(size = 0.3, color = "grey50"),
axis.text = element_text(size = 9.5, color = "black"),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
strip.text = element_text(size = 9.5, color = "black")
)
#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for forest plots
#++++++++++++++++++++++++++++++++++++++++++++++#
stackedbartheme <- theme(
strip.text = element_text(size = 13),
axis.text.y = element_text(size = 13),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.ticks.x = element_blank(),
legend.text = element_text(size = 11),
legend.background = element_rect("grey95")
)
#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for regression scatterplot
#++++++++++++++++++++++++++++++++++++++++++++++#
regressiontheme <- theme_bw() +
theme(
legend.position = "none",
axis.ticks = element_line(size = 0.3, color = "grey50"),
axis.text = element_text(size = 9.5, color = "black"),
axis.text.y = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "grey50", size = 0.7),
strip.text = element_text(size = 9.5, color = "black")
)
#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for orchard plot
#++++++++++++++++++++++++++++++++++++++++++++++#
orchardtheme <- theme_bw() +
theme(
panel.grid.major.y = element_blank(),
axis.text.y = element_text(size = 14, color = "black", face = "italic"),
axis.text.x = element_text(size = 14, color = "grey20"),
axis.title = element_text(size = 14, color = "black"),
legend.position = c(0.15, 0.002),
legend.justification = c(0.15, 0.002),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
legend.direction = "horizontal"
)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Correcting estimate of binomial regression
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
c2 <- (16 * sqrt(3)/(15 * pi))^2
tibble(
Factors = c(
"Genetic divergence",
"Phenotypic divergence in focal trait",
"Inviability of reciprocal hybrids (i.e. genetic incompatibility)",
"Distribution overlap between parental species",
"Crossing direction (parent-of-origin effect)",
"Sex-determination system (male heterogamety vs. female heterogamety)",
"Interaction between crossing direction and sex-determination system",
"Trait type (sound trait vs. morphology)"
),
'Hypothetical effect for novelty in phenotypic means' = c(
"Increase. Positively correlate with the genetic divergence at phenotype-determining loci that increase phenotypic novelty through novel allelic interactions (i.e. over/underdominance, dominance, and epistasis) [@Lamkey1999; @Rieseberg1999]",
"Increase. Indicates the genetic divergence at quantitative trait loci (QTL) that enhances phenotypic novelty [@Lamkey1999]",
"Reduces trait size by outbreeding depression",
"Increase. Reinforcement in range overlapping species pairs diverges QTL to develop behavioral isolation, and thus facilitate phenotypic novelty.",
"Genomic imprinting increases novel phenotype resembles either mother or father (i.e. polar overdominance) [@Cockett1996].",
"Increase in male heterogamety. In male heterogametic organisms, coadaptation between sex-chromosome and other chromosomes breaks down in F1 males [@Haldane1922; @Schilthuizen2011]. This genetic incompatibility reduce trait size in male heterogametic hybrids.",
"Male heterogametic organisms tend to exhibit novel phenotype similar to father species, not mother species, due to effect of Y chromosome. Female heterogametic organisms do not show such trend.",
"Increase in sound because of lower heritability compared to morphology [@Hoffmann2016]"
),
`Hypothetical effect for phenotypic variability` = c(
"Increase. Increase variation because of increased diversity in novel allelic interactions at phenotype determining loci [@Edmands1999]; increase variation by damaging developmental stability, through genetic incompatibility [@Lerner1954; @Alibert2003].",
"Increase. Increases diversity in novel allelic interactions at QTL [@Edmands1999]",
"Reduce. Lethal allelic interactions reduce genetic diversity of surviving hybrids",
"Reduce. Reinforcement reduces intraspecific variation at QTL, and thus diminish genetic diversity of F1 hybrids at QTL",
"Not specified",
"Increase in male heterogamety. Reduced developmental stability increases phenotypic variability of heterogametic F1 males",
"Not specified",
"Not specified"
)
) %>%
kable("html", caption = "Table S1. Hypotheses for phenotypic novelty and variability of F1 hybrids") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Factors | Hypothetical effect for novelty in phenotypic means | Hypothetical effect for phenotypic variability |
|---|---|---|
| Genetic divergence | Increase. Positively correlate with the genetic divergence at phenotype-determining loci that increase phenotypic novelty through novel allelic interactions (i.e. over/underdominance, dominance, and epistasis) (Lamkey and Edwards 1999; Rieseberg et al. 1999) | Increase. Increase variation because of increased diversity in novel allelic interactions at phenotype determining loci (Edmands 1999); increase variation by damaging developmental stability, through genetic incompatibility (Lerner 1954; Alibert and Auffray 2003). |
| Phenotypic divergence in focal trait | Increase. Indicates the genetic divergence at quantitative trait loci (QTL) that enhances phenotypic novelty (Lamkey and Edwards 1999) | Increase. Increases diversity in novel allelic interactions at QTL (Edmands 1999) |
| Inviability of reciprocal hybrids (i.e. genetic incompatibility) | Reduces trait size by outbreeding depression | Reduce. Lethal allelic interactions reduce genetic diversity of surviving hybrids |
| Distribution overlap between parental species | Increase. Reinforcement in range overlapping species pairs diverges QTL to develop behavioral isolation, and thus facilitate phenotypic novelty. | Reduce. Reinforcement reduces intraspecific variation at QTL, and thus diminish genetic diversity of F1 hybrids at QTL |
| Crossing direction (parent-of-origin effect) | Genomic imprinting increases novel phenotype resembles either mother or father (i.e. polar overdominance) (Cockett et al. 1996). | Not specified |
| Sex-determination system (male heterogamety vs. female heterogamety) | Increase in male heterogamety. In male heterogametic organisms, coadaptation between sex-chromosome and other chromosomes breaks down in F1 males (Haldane 1922; Schilthuizen et al. 2011). This genetic incompatibility reduce trait size in male heterogametic hybrids. | Increase in male heterogamety. Reduced developmental stability increases phenotypic variability of heterogametic F1 males |
| Interaction between crossing direction and sex-determination system | Male heterogametic organisms tend to exhibit novel phenotype similar to father species, not mother species, due to effect of Y chromosome. Female heterogametic organisms do not show such trend. | Not specified |
| Trait type (sound trait vs. morphology) | Increase in sound because of lower heritability compared to morphology (Hoffmann et al. 2016) | Not specified |
tibble(
Taxon = c(
"Anura: tree frog (*Hyla*)",
"Diptera: fruit fly (*Drosophila*)",
"Orthoptera: bushcricket (*Ephippiger*)",
"Orthoptera: grasshopper (*Chorthippus*)",
"Orthoptera: grasshopper (*Chorthippus*)",
"Orthoptera: grasshopper (*Chorthippus*)",
"Orthoptera: Hawaiian cricket (*Laupala*)"
),
`Parental species` = c(
"*H. chrysoscelis* × *H. femoralis*",
"*D. virilis* female × *D. montana* male",
"*E. ephippiger* polysyllabic form
× *E. ephippiger* monosyllabic form",
"*C. brunneus* × *C. jacobsi*",
"*C. biguttulus* × *C. Brunneus*",
"*C. parallelus* × *C. Montanus*",
"*L. kohalensis* × *L. Paranigra*"
),
`Reciprocal cross` = c("Viable", "Inviable", rep("Viable", 5)),
Results = c(
"**Dominance**. Both reciprocal hybrids preferred hybrids over one parental species but not over the other parental species",
"**Maternal / paternal inheritance**. Resembled mother in their receptivity, but resembled father in their song requirement",
"**Additive**. Intermediate mate preferences without a large difference between reciprocal crosses",
"**Dominance and paternal inheritance**. Both reciprocal hybrids preferred one parental species over themselves. Preference function resembles that of the father",
"**Additive and dominance**. Several components of preference showed dominance, but other components showed additive inheritance. No maternal/paternal inheritance.",
"**Novel preference**. Both reciprocal hybrids did not discriminate between males of two parental species",
"**Additive**. Intermediate preference function, which was similar to reciprocal hybrids, resulting in preference for hybrids"
),
Reference = c(
"@Doherty1984", "@Isoherranen1999", "@Ritchie2000", "@Bridle2006",
"@Gottsberger2019", "@Hochkirch2011", "@Shaw2000"
)
) %>%
kable("html", caption = "Table S2. Previously described female mate preference of F1 hybrids") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Taxon | Parental species | Reciprocal cross | Results | Reference |
|---|---|---|---|---|
| Anura: tree frog (Hyla) | H. chrysoscelis × H. femoralis | Viable | Dominance. Both reciprocal hybrids preferred hybrids over one parental species but not over the other parental species | Doherty and Gerhardt (1984) |
| Diptera: fruit fly (Drosophila) | D. virilis female × D. montana male | Inviable | Maternal / paternal inheritance. Resembled mother in their receptivity, but resembled father in their song requirement | Isoherranen et al. (1999) |
| Orthoptera: bushcricket (Ephippiger) | E. ephippiger polysyllabic form × E. ephippiger monosyllabic form | Viable | Additive. Intermediate mate preferences without a large difference between reciprocal crosses | Ritchie (2000) |
| Orthoptera: grasshopper (Chorthippus) | C. brunneus × C. jacobsi | Viable | Dominance and paternal inheritance. Both reciprocal hybrids preferred one parental species over themselves. Preference function resembles that of the father | Bridle et al. (2006) |
| Orthoptera: grasshopper (Chorthippus) | C. biguttulus × C. Brunneus | Viable | Additive and dominance. Several components of preference showed dominance, but other components showed additive inheritance. No maternal/paternal inheritance. | Gottsberger and Mayer (2019) |
| Orthoptera: grasshopper (Chorthippus) | C. parallelus × C. Montanus | Viable | Novel preference. Both reciprocal hybrids did not discriminate between males of two parental species | Hochkirch and Lemke (2011) |
| Orthoptera: Hawaiian cricket (Laupala) | L. kohalensis × L. Paranigra | Viable | Additive. Intermediate preference function, which was similar to reciprocal hybrids, resulting in preference for hybrids | Shaw (2000) |
Only 3 out of 7 studies detected additive inheritance in female mate preference during hybridization, showing that non-additivity pervades female mate preference of F1 hybrids. Dominance and parent-of-origin effect (maternal / paternal inheritance) were detected in 3 and 2 studies, respectively. Novel weak preference also appeared in 1 study. Importantly, components of female preference often varied in inheritance mode, indicating that integration of mate preference easily breaks down in F1 hybrids. This summary is based on a non-exhaustive and non-systematic review.
List of primary studies included
read.xlsx(
"../data/original.data.xlsx", sheet = "Primary.studies"
# Same data is also saved as "../data/original.data.Primary.studies.txt"
) %>%
arrange(First.author, Year) %>%
kable("html") %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Study.name | First.author | Year | Title | Journal |
|---|---|---|---|---|
| Bentrey&Hoy_1972 | Bentley, D. R. | 1972 | Genetic control of the neuronal network generating cricket (Teleogryllus gryllus) song patterns | Animal Behaviour |
| Blankers.et.al_2015 | Blankers, T. | 2015 | Phenotypic variation and covariation indicate high evolvability of acoustic communication in crickets | Journal of Evolutionary Biology |
| Butlin&Hewitt_1988 | Butlin, R. K. | 1988 | Genetics of behavioural and morphological differences between parapatric subspecies of Chorthippus parallelus (Orthoptera: Acrididae) | Biological Journal of the Linnean Society |
| Carson.et.al_1994 | Carson, H. L. | 1994 | Change in male secondary sexual characters in artificial interspecific hybrid populations | Proceedings of the National Academy of Sciences of the United States of America |
| Coyne_1983 | Coyne, J.A. | 1983 | Genetic basis of differences in genital morphology among three sibling species of Drosophila | Evolution |
| Coyne_1985 | Coyne, J.A. | 1985 | Genetic studies of three sibling species of Drosophila with relationship to theories of speciation | Genetical Research |
| Coyne_1986 | Coyne, J.A. | 1986 | Meiotic segregation and male recombination in interspecific hybrids of Drosophila | Genetics |
| Coyne.et.al_1991 | Coyne, J.A. | 1991 | Genetics of morphological differences and hybrid sterility between Drosophila sechellia and its relatives | Genetical Research |
| Crapon&Fritzsch_1984 | Crapon de caprona, MD. | 1984 | Interspecific fertile hybrids of haplochromine cichlidae (Teleostei) and their possible importance for speciation | Netherlands Journal of Zoology |
| Deregnaucourt_2010 | Deregnaucourt, S. | 2010 | Interspecific hybridization as a tool to understand vocal divergence: The example of crowing in quail (Genus Coturnix) | PLoS ONE |
| Doherty & Gerhardt 1984 | Doherty, J. A. | 1984 | Acoustic communication in hybrid treefrogs: sound production by males and selective phonotaxis by females | Journal of Comparative Physiology A |
| Ewing_1969 | Ewing, A.W. | 1969 | The genetic basis of sound production in Drosophila pseudoobscura and D. persimilis | Animal Behaviour |
| Gadenne et al_1997 | Gadenne, C. | 1997 | Development and pheromone communication systems in hybrids of Agrotis ipsilon and Agrotis segetum (Lepidoptera: Noctuidae) | Journal of Chemical Ecology |
| Gottsberger&Mayer_2007 | Gottsberger, B. | 2007 | Behavioral sterility of hybrid males in acoustically communicating grasshoppers (Acrididae, Gomphocerinae) | Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology |
| Magalhaes&Seehausen_2010 | Magalhaes, I. S. | 2010 | Genetics of male nuptial colour divergence between sympatric sister species of a Lake Victoria cichlid fish | Journal of Evolutionary Biology |
| Monti et al_2001 | Monti, L | 2001 | Elliptic fourier analysis of the form of genitalia in two Spodoptera species and their hybrids (Lepidoptera: Noctuidae) | Biological Journal of the Linnean Society |
| Musolf et al_2015 | Musolf, K | 2015 | Ultrasonic vocalizations of male mice differ among species and females show assortative preferences for male calls | PLoS ONE |
| Paallysaho et al_2003 | Paallysaho, S. | 2003 | Role of X chromosomal song genes in the evolution of species-specific courtship songs in Drosophila virilis group species | Behavior Genetics |
| Sasabe_et.al_2007 | Sasabe, M. | 2007 | The genetic basis of interspecific differences in genital morphology of closely related carabid beetles | Heredity |
| Shaw_1996 | Shaw, K. L. | 1996 | Polygenic inheritance of a behavioral phenotype: Interspecific genetics of song in the Hawaiian cricket genus Laupala | Evolution |
| Shaw_2000 | Shaw, K. L. | 2000 | Interspecific genetics of mate recognition: Inheritance of female acoustic preference in Hawaiian crickets | Evolution |
| Suvanto et al_1994 | Suvanto, L. | 1994 | Secondary courtship songs and inhibitory songs of Drosophila virilis-group males | Behavior Genetics |
| Tomaru&Oguma_1994 | Tomaru, M. | 1994 | Differences in courtship song in the species of the Drosophila auraria complex | Animal Behaviour |
| van der Sluijs et al_2008 | Van Der Sluijs, I. | 2008 | Female mating preference functions predict sexual selection against hybrids between sibling species of cichlid fish | Philosophical Transactions of the Royal Society B: Biological Sciences |
| Wells_1994 | Wells, M. M. | 1994 | Behavioral responses of hybrid lacewings (Neuroptera: Chrysopidae) to courtship songs | Journal of Insect Behavior |
Study.name: Including author name(s) and published year
Data of phenotype used in this study.
## Phenotype data ##
pheno <- read.xlsx(
"../data/original.data.xlsx", sheet = "Phenotype"
# Same data is also saved as "../data/original.data.Phenotype.txt"
) %>%
# Excluding data without any parent data
drop_na(contains("SD.sp")) %>%
# to numeric
mutate_at(
vars(contains("Mn") | contains("SD") | N.sp1:N.sp2),
as.numeric
) %>%
# delete observation with negative mean trait value
filter(Mn.sp1 > 0 & Mn.sp2 > 0 & homologous == "Yes") %>%
# calculate phenotypic difference between parental species
mutate(
Pheno.divergence = abs(log(Mn.sp1/Mn.sp2))
) %>%
mutate_at(vars(contains("pheno.div")), scale) %>%
# describe whether full cross or not
mutate(
Reciprocal = ifelse(
is.na(Mn.hyb12 & Mn.hyb21),
"Inviable", "Viable"
)
)
# making a scrollable table
kable(pheno, "html") %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Study.ID | Study.name | Source | Cross.ID | Genus | sp1.name | sp2.name | species.pair | homologous | trait.type | trait | Mn.sp1 | SD.sp1 | SE.sp1 | Mn.hyb12 | SD.hyb12 | SE.hyb12 | Mn.hyb21 | SD.hyb21 | SE.hyb21 | Mn.sp2 | SD.sp2 | SE.sp2 | Parents.sp1F.sp1 | Parents.sp1M.sp1 | Parents.sp1F.hyb12 | Parents.sp1M.hyb21 | Parents.sp2F.hyb21 | Parents.sp2M.hyb12 | Parents.sp2F.sp2 | Parents.sp2M.sp2 | N.sp1 | N.hyb12 | N.hyb21 | N.sp2 | reps.sp1 | reps.hyb12 | reps.hyb21 | reps.sp2 | Pheno.divergence | Reciprocal |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| s001 | Blankers.et.al_2015 | table1 | Gryllus_rubens_Gryllus_texensis | Gryllus | Gryllus_rubens | Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Yes | sound | Carrier frequency | 4.7300000 | 0.2700000 | 4.8200000 | 0.2700000 | 5.0100000 | 0.1700000 | 5.1800000 | 0.2200000 | 12 | 12 | 12 | 12 | 73 | 22 | 28 | 44 | -0.544841228 | Viable | ||||||||||||
| s001 | Blankers.et.al_2015 | table1 | Gryllus_rubens_Gryllus_texensis | Gryllus | Gryllus_rubens | Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Yes | sound | Pulse rate | 45.3400000 | 3.8600000 | 55.2700000 | 3.9600000 | 61.9600000 | 2.7900000 | 66.8800000 | 5.4000000 | 12 | 12 | 12 | 12 | 73 | 22 | 28 | 44 | -0.086102242 | Viable | ||||||||||||
| s001 | Blankers.et.al_2015 | table1 | Gryllus_rubens_Gryllus_texensis | Gryllus | Gryllus_rubens | Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Yes | sound | Pulse duty cycle | 0.4300000 | 0.0600000 | 0.4600000 | 0.0800000 | 0.4800000 | 0.0800000 | 0.4400000 | 0.0800000 | 12 | 12 | 12 | 12 | 73 | 22 | 28 | 44 | -0.649410595 | Viable | ||||||||||||
| s001 | Blankers.et.al_2015 | table1 | Gryllus_rubens_Gryllus_texensis | Gryllus | Gryllus_rubens | Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Yes | sound | Trill rate | 0.2800000 | 0.1500000 | 0.5900000 | 0.2500000 | 0.6800000 | 0.3500000 | 1.2800000 | 0.3900000 | 12 | 12 | 12 | 12 | 73 | 22 | 28 | 44 | 1.656119587 | Viable | ||||||||||||
| s001 | Blankers.et.al_2015 | table1 | Gryllus_rubens_Gryllus_texensis | Gryllus | Gryllus_rubens | Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Yes | sound | Trill duty cycle | 0.7800000 | 0.1000000 | 0.7100000 | 0.1000000 | 0.7300000 | 0.1400000 | 0.6200000 | 0.1100000 | 12 | 12 | 12 | 12 | 73 | 22 | 28 | 44 | -0.331214279 | Viable | ||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | EI | 14.9600000 | 3.0958682 | 12.5000000 | 1.8973666 | 11.6000000 | 2.3216374 | 9.1888889 | 1.5863616 | 10 | 7 | 12 | 9 | 0.065883205 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | EI | 14.9600000 | 3.0958682 | 12.7666667 | 1.7461068 | 11.7400000 | 2.9499831 | 10.7880000 | 2.4703554 | 10 | 3 | 10 | 25 | -0.181236570 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | SI | 15.1375000 | 11.2491875 | 0.5000000 | 9.7000000 | 6.2282522 | 11.8750000 | 6.6447348 | 8 | 1 | 10 | 6 | -0.310936151 | Viable | |||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | SI | 15.1375000 | 11.2491875 | 10.4250000 | 10.2530483 | 6.9305556 | 5.6248059 | 11.4291667 | 11.0768873 | 8 | 3 | 9 | 24 | -0.251995019 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | ES | 31.6666667 | 26.6666667 | 60.7142857 | 12.9362645 | 43.6666667 | 18.5711844 | 59.4444444 | 25.4344959 | 10 | 7 | 15 | 9 | 0.285206116 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | ES | 31.6666667 | 26.6666667 | 31.6666667 | 9.4280904 | 52.2727273 | 18.1363067 | 44.1666667 | 25.3174292 | 10 | 3 | 11 | 10 | -0.172364334 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | SH | 4.1538462 | 12.6217151 | 2.3571429 | 2.8996833 | 9.2142857 | 5.3375833 | 5.9545455 | 4.6390367 | 26 | 14 | 14 | 11 | -0.130138747 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | SH | 4.1538462 | 12.6217151 | 4.7857143 | 5.0345743 | 7.9166667 | 5.5295921 | 7.3666667 | 5.0793700 | 26 | 7 | 12 | 30 | 0.197646553 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | EH | 63.0769231 | 66.4928701 | 108.5714286 | 102.7380263 | 194.2857143 | 83.6416067 | 200.0000000 | 120.3328717 | 26 | 14 | 14 | 10 | 1.092591646 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | EH | 63.0769231 | 66.4928701 | 77.1428571 | 73.6234213 | 223.0769231 | 116.8420772 | 181.3333333 | 123.2810700 | 26 | 7 | 13 | 30 | 0.941675478 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | COH | 4.9800000 | 5.9337678 | 7.8571429 | 6.9755404 | 6.4285714 | 5.0350810 | 2.5000000 | 3.9080337 | 25 | 14 | 14 | 11 | 0.376638926 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | COH | 4.9800000 | 5.9337678 | 3.5000000 | 4.8989795 | 10.1923077 | 8.6057639 | 3.7666667 | 4.6110977 | 25 | 8 | 13 | 30 | -0.254716809 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | COD | 60.4615385 | 13.8153504 | 51.6000000 | 7.2000000 | 58.2857143 | 17.9181814 | 44.4000000 | 11.7575508 | 13 | 10 | 14 | 5 | -0.209234963 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | COD | 60.4615385 | 13.8153504 | 66.0000000 | 9.7979590 | 55.0909091 | 11.9503103 | 43.5000000 | 14.6201915 | 13 | 3 | 11 | 16 | -0.177692520 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | EL | 3.0400000 | 0.4983974 | 3.1857143 | 0.4517540 | 2.8076923 | 0.4269058 | 2.2250000 | 0.2633913 | 10 | 7 | 13 | 8 | -0.204101811 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | EL | 3.0400000 | 0.4983974 | 2.6333333 | 0.1885618 | 2.9181818 | 0.3242283 | 2.1416667 | 0.5626697 | 10 | 3 | 11 | 24 | -0.145305786 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | NS | 13.9000000 | 2.6153394 | 15.6666667 | 2.0344259 | 13.8846154 | 1.6426274 | 12.0000000 | 1.5000000 | 10 | 6 | 13 | 10 | -0.458428548 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | NS | 13.9000000 | 2.6153394 | 12.1666667 | 0.4714045 | 13.6818182 | 1.1922615 | 12.7000000 | 2.4819347 | 10 | 3 | 11 | 25 | -0.545754618 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | SL | 0.2270000 | 0.0124900 | 0.2121429 | 0.0174964 | 0.2065385 | 0.0274131 | 0.1987500 | 0.0264280 | 10 | 7 | 13 | 8 | -0.480115420 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | SL | 0.2270000 | 0.0124900 | 0.2183333 | 0.0205480 | 0.2168182 | 0.0224897 | 0.1750000 | 0.0284376 | 10 | 3 | 11 | 23 | -0.284098069 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | COR | 3.5000000 | 3.2730000 | 3.3181818 | 3.1298943 | 3.2500000 | 3.0741110 | 2.5000000 | 2.4074367 | 14 | 11 | 12 | 4 | -0.166562938 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | sound | COR | 3.5000000 | 3.2730000 | 3.0000000 | 2.8262465 | 2.5000000 | 2.3614732 | 1.6333333 | 1.4974284 | 14 | 2 | 11 | 15 | 0.489079925 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | morphology | RL | 4.0142857 | 0.2799417 | 3.7545455 | 0.1724879 | 3.7428571 | 0.1545236 | 3.5571429 | 0.3499271 | 21 | 11 | 14 | 7 | -0.498599402 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | morphology | RL | 4.0142857 | 0.2799417 | 3.9111111 | 0.1940472 | 3.7000000 | 0.2449490 | 3.4481481 | 0.2283116 | 21 | 18 | 16 | 27 | -0.450665719 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | morphology | NP | 146.4285714 | 14.2380156 | 133.3333333 | 10.6718737 | 119.2857143 | 9.0350790 | 112.1428571 | 10.3015751 | 21 | 12 | 14 | 27 | -0.273932094 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | morphology | NP | 146.4285714 | 14.2380156 | 126.5000000 | 9.6306801 | 106.2500000 | 17.9843682 | 94.2592593 | 11.1971974 | 21 | 20 | 16 | 7 | -0.006349655 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityA | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | morphology | PD | 36.5294118 | 3.1081988 | 34.3333333 | 2.1081851 | 31.3636364 | 2.2268089 | 30.2000000 | 3.9191836 | 17 | 9 | 11 | 5 | -0.391744730 | Viable | ||||||||||||||||
| s002 | Butlin&Hewitt_1988 | Fig1 | parallelus_localityB | Chorthippus | Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | Yes | morphology | PD | 36.5294118 | 3.1081988 | 32.5294118 | 2.1176471 | 29.3333333 | 3.5433819 | 27.2222222 | 1.8724777 | 17 | 17 | 12 | 18 | -0.231852056 | Viable | ||||||||||||||||
| s003 | Carson.et.al_1994 | table4 | Drosophila_heteroneura_Drosophila_silvestris | Drosophila | Drosophila_heteroneura | Drosophila_silvestris | Drosophila_heteroneura_Drosophila_silvestris | Yes | morphology | Number of tibial cilia on male foreleg | 37.2500000 | 1.6500000 | 45.1500000 | 2.7600000 | 59.5700000 | 3.6700000 | 72.8500000 | 6.3300000 | 35 | 35 | 35 | 35 | 35 | 35 | 35 | 35 | 20 | 20 | 20 | 20 | 0.348315583 | Viable | ||||||||
| s004 | Coyne_1983 | table3 | BGxMutant | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | GA | 1442.0000000 | 202.6573463 | 37.000 | 5141.0000000 | 506.6665570 | 91.000 | 12166.0000000 | 735.0000000 | 147.0000 | 3 | 3 | 20 | 20 | 20 | 20 | 3 | 3 | 30 | 0 | 31 | 25 | 2.599978426 | Inviable | |||||||
| s004 | Coyne_1983 | table3 | BGxMutant | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | WL | 462.0000000 | 10.9544512 | 2.000 | 487.0000000 | 11.1355287 | 2.000 | 477.0000000 | 10.0000000 | 2.0000 | 3 | 3 | 20 | 20 | 20 | 20 | 3 | 3 | 30 | 0 | 31 | 25 | -0.635606580 | Inviable | |||||||
| s004 | Coyne_1983 | table3 | BGxMutant | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | GA/WL | 3.1200000 | 0.4381780 | 0.080 | 10.5800000 | 2.0600728 | 0.370 | 25.4900000 | 1.5000000 | 0.3000 | 3 | 3 | 20 | 20 | 20 | 20 | 3 | 3 | 30 | 0 | 31 | 25 | 2.550441963 | Inviable | |||||||
| s004 | Coyne_1983 | table3 | BGxSPD(wildtype) | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | GA | 1442.0000000 | 202.6573463 | 37.000 | 5139.0000000 | 471.0413995 | 86.000 | 5400.0000000 | 651.7898434 | 119.000 | 12050.0000000 | 1007.8095058 | 184.0000 | 3 | 3 | 20 | 20 | 20 | 20 | 3 | 3 | 30 | 30 | 30 | 30 | 2.585221856 | Viable | ||||
| s004 | Coyne_1983 | table3 | BGxSPD(wildtype) | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | WL | 462.0000000 | 10.9544512 | 2.000 | 497.0000000 | 10.9544512 | 2.000 | 487.0000000 | 10.9544512 | 2.000 | 1263.0000000 | 27.3861279 | 5.0000 | 3 | 3 | 20 | 20 | 20 | 20 | 3 | 3 | 30 | 30 | 30 | 30 | 0.864197270 | Viable | ||||
| s004 | Coyne_1983 | table3 | BGxSPD(wildtype) | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | GA/WL | 3.1200000 | 0.4381780 | 0.080 | 10.3500000 | 0.9859006 | 0.180 | 11.0900000 | 1.2597619 | 0.230 | 9.5380000 | 0.7558571 | 0.1380 | 3 | 3 | 20 | 20 | 20 | 20 | 3 | 3 | 30 | 30 | 30 | 30 | 1.036354019 | Viable | ||||
| s005 | Coyne_1985 | table1&2 | BGxOxnard | Drosophila | Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | Yes | morphology | Sex comb tooth number | 14.3100000 | 1.1313708 | 0.080 | 12.3300000 | 1.0148892 | 0.100 | 12.1000000 | 0.9899495 | 0.070 | 9.8600000 | 0.8485281 | 0.0600 | 200 | 103 | 200 | 200 | -0.111112971 | Viable | ||||||||||||
| s006 | Coyne_1986 | table1 | FC | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6T | 176.0000000 | 44.5477272 | 10.500 | 142.0000000 | 36.9109740 | 8.700 | 15.7000000 | 2.6153394 | 0.6000 | 8 | 8 | 18 | 18 | 19 | 3.037736912 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | FC | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 7T | 49.3000000 | 12.3036580 | 2.900 | 393.9000000 | 97.5807358 | 23.000 | 335.2000000 | 61.0245852 | 14.0000 | 8 | 8 | 18 | 18 | 19 | 2.267571704 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | FC | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6Tratio | 0.7790000 | 0.0169706 | 0.004 | 0.2640000 | 0.0169706 | 0.004 | 0.0490000 | 0.0305123 | 0.0070 | 8 | 8 | 18 | 18 | 19 | 3.575856725 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | f^2 | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6T | 176.0000000 | 44.5477272 | 10.500 | 142.7000000 | 11.1323852 | 2.700 | 17.9000000 | 6.1846584 | 1.5000 | 8 | 8 | 18 | 17 | 17 | 2.835746058 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | f^2 | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 7T | 49.3000000 | 12.3036580 | 2.900 | 459.0000000 | 53.6003731 | 13.000 | 424.2000000 | 78.3390069 | 19.0000 | 8 | 8 | 18 | 17 | 17 | 2.630270622 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | f^2 | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6Tratio | 0.7790000 | 0.0169706 | 0.004 | 0.2380000 | 0.0164924 | 0.004 | 0.0400000 | 0.0123693 | 0.0030 | 8 | 8 | 18 | 17 | 17 | 3.888440179 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | RMdet | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6T | 176.0000000 | 44.5477272 | 10.500 | 142.8000000 | 35.0000000 | 7.000 | 26.9000000 | 10.0697567 | 2.6000 | 8 | 8 | 18 | 25 | 15 | 2.208355177 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | RMdet | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 7T | 49.3000000 | 12.3036580 | 2.900 | 385.2000000 | 67.5000000 | 13.500 | 541.9000000 | 199.0713440 | 51.4000 | 8 | 8 | 18 | 25 | 15 | 3.007446168 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | RMdet | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6Tratio | 0.7790000 | 0.0169706 | 0.004 | 0.2690000 | 0.0350000 | 0.007 | 0.0480000 | 0.0034857 | 0.0009 | 8 | 8 | 18 | 25 | 15 | 3.607615970 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | RM | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6T | 176.0000000 | 44.5477272 | 10.500 | 184.2000000 | 50.0000000 | 10.000 | 25.0000000 | 5.4221767 | 1.4000 | 8 | 8 | 18 | 25 | 15 | 2.321180579 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | RM | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 7T | 49.3000000 | 12.3036580 | 2.900 | 381.7000000 | 73.0000000 | 14.600 | 612.6000000 | 115.4149037 | 29.8000 | 8 | 8 | 18 | 25 | 15 | 3.196330430 | Inviable | ||||||||||||||
| s006 | Coyne_1986 | table1 | RM | Drosophila | Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Yes | morphology | 6Tratio | 0.7790000 | 0.0169706 | 0.004 | 0.3220000 | 0.0400000 | 0.008 | 0.0390000 | 0.0023238 | 0.0006 | 8 | 8 | 18 | 25 | 15 | 3.927436409 | Inviable | ||||||||||||||
| s008 | Coyne.et.al_1991 | table4 | pn_j_irX85 | Drosophila | Drosophila_mauritiana | Drosophila_sechellia | Drosophila_mauritiana_Drosophila_sechellia | Yes | morphology | Genital area | 1.7460000 | 0.2100000 | 0.035 | 3.3230000 | 0.4501244 | 0.074 | 3.1040000 | 0.3710485 | 0.061 | 5.3110000 | 0.5231176 | 0.0860 | 36 | 37 | 37 | 37 | 1.028655549 | Viable | ||||||||||||
| s008 | Coyne.et.al_1991 | table4 | pn_j_irX85 | Drosophila | Drosophila_mauritiana | Drosophila_sechellia | Drosophila_mauritiana_Drosophila_sechellia | Yes | morphology | Tibia length | 0.4410000 | 0.0120000 | 0.002 | 0.4740000 | 0.0121655 | 0.002 | 0.4870000 | 0.0182483 | 0.003 | 0.4690000 | 0.0182483 | 0.0030 | 36 | 37 | 37 | 37 | -0.590004930 | Viable | ||||||||||||
| s009 | Crapon&Fritzsch_1984 | table4 | Haplochromis_burtoni_Haplochromis_nubilus | Haplochromis | Haplochromis_burtoni | Haplochromis_nubilus | Haplochromis_burtoni_Haplochromis_nubilus | Yes | morphology | egg dummy size | 8.9000000 | 0.7000000 | 10.0000000 | 1.6000000 | 8.3000000 | 0.8000000 | 48 | 34 | 58 | -0.577316397 | Inviable | |||||||||||||||||||
| s009 | Crapon&Fritzsch_1984 | table4 | Haplochromis_burtoni_Haplochromis_nubilus | Haplochromis | Haplochromis_burtoni | Haplochromis_nubilus | Haplochromis_burtoni_Haplochromis_nubilus | Yes | morphology | egg dummy number | 6.7000000 | 1.8000000 | 4.3000000 | 1.0000000 | 2.5000000 | 0.6000000 | 48 | 34 | 58 | 0.833602237 | Inviable | |||||||||||||||||||
| s009 | Crapon&Fritzsch_1984 | table4 | Haplochromis_burtoni_Haplochromis_nubilus | Haplochromis | Haplochromis_burtoni | Haplochromis_nubilus | Haplochromis_burtoni_Haplochromis_nubilus | Yes | morphology | egg dummy position | 1.4000000 | 0.6000000 | 3.9000000 | 1.2000000 | 5.4000000 | 0.6000000 | 48 | 34 | 58 | 1.394429398 | Inviable | |||||||||||||||||||
| s010 | Deregnaucourt_2010 | table2 | Coturnix_coturnix_coturnix_Coturnix_japonica | Coturnix | Coturnix_coturnix_coturnix | Coturnix_japonica | Coturnix_coturnix_coturnix_Coturnix_japonica | Yes | sound | S2 | 172.0000000 | 86.6833317 | 17.000 | 168.0000000 | 59.2368129 | 11.000 | 182.0000000 | 54.7448628 | 9.000 | 59.0000000 | 79.3725393 | 15.0000 | 8 | 8 | 15 | 15 | 26 | 29 | 37 | 28 | 0.963200823 | Viable | ||||||||
| s11 | Doherty & Gerhardt 1984 | fig4 | Hyla_chrysoscelis_Hyla_femoralis | Hyla | Hyla_chrysoscelis | Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Yes | sound | pulse/call | 29.3023256 | 3.2558140 | 15.3488372 | 4.1860465 | 20.0000000 | 9.3023256 | 4.2420000 | 0.6225769 | 5 | 5 | 4 | 5 | 2.291952220 | Viable | ||||||||||||||||
| s11 | Doherty & Gerhardt 1984 | fig4 | Hyla_chrysoscelis_Hyla_femoralis | Hyla | Hyla_chrysoscelis | Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Yes | sound | pulse duration | 15.3191489 | 1.2765957 | 17.0212766 | 8.5106383 | 17.8723404 | 7.2340426 | 48.5106383 | 7.2340426 | 6 | 6 | 5 | 5 | 1.090615675 | Viable | ||||||||||||||||
| s11 | Doherty & Gerhardt 1984 | fig4 | Hyla_chrysoscelis_Hyla_femoralis | Hyla | Hyla_chrysoscelis | Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Yes | sound | pulse rise | 7.6068376 | 1.1965812 | 2.3931624 | 1.8803419 | 1.9658120 | 1.1965812 | 1.3675214 | 0.4273504 | 6 | 6 | 5 | 5 | 1.958354051 | Viable | ||||||||||||||||
| s11 | Doherty & Gerhardt 1984 | fig4 | Hyla_chrysoscelis_Hyla_femoralis | Hyla | Hyla_chrysoscelis | Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Yes | sound | pulse decay | 7.7142857 | 2.0571429 | 10.2857143 | 4.8857143 | 8.4000000 | 3.2571429 | 4.8000000 | 1.7142857 | 6 | 5 | 5 | 5 | 0.045972199 | Viable | ||||||||||||||||
| s11 | Doherty & Gerhardt 1984 | fig4 | Hyla_chrysoscelis_Hyla_femoralis | Hyla | Hyla_chrysoscelis | Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Yes | sound | pulse period | 26.6304348 | 5.9782609 | 78.2608696 | 23.9130435 | 80.4347826 | 15.7608696 | 157.6086957 | 60.3260870 | 5 | 5 | 5 | 5 | 2.053870707 | Viable | ||||||||||||||||
| s12 | Ewing_1969 | table2 | Drosophila_persimilis_Drosophila_pseudoobscura | Drosophila | Drosophila_persimilis | Drosophila_pseudoobscura | Drosophila_persimilis_Drosophila_pseudoobscura | Yes | sound | Mean HRR song pulse intervals in msec | 66.8000000 | 10.9000000 | 48.7000000 | 7.9000000 | 56.8000000 | 7.3000000 | 42.6000000 | 4.8000000 | 25 | 25 | 25 | 25 | 4 | 4 | 4 | 4 | 386 | 273 | 268 | 281 | 0.008067489 | Viable | ||||||||
| s13 | van der Sluijs et al_2008 | table1 | Pundamilia_nyererei_Pundamilia_pundamilia | Pundamilia | Pundamilia_nyererei | Pundamilia_pundamilia | Pundamilia_nyererei_Pundamilia_pundamilia | Yes | morphology | colour score | 3.8900000 | 0.2121320 | 0.050 | 2.2100000 | 0.8668333 | 0.170 | 2.2500000 | 0.8049845 | 0.180 | 0.2100000 | 0.3487119 | 0.0800 | 18 | 26 | 20 | 19 | 3.811311702 | Viable | ||||||||||||
| s13 | van der Sluijs et al_2008 | table1 | Pundamilia_nyererei_Pundamilia_pundamilia | Pundamilia | Pundamilia_nyererei | Pundamilia_pundamilia | Pundamilia_nyererei_Pundamilia_pundamilia | Yes | morphology | n bars | 6.3600000 | 0.4666905 | 0.110 | 5.3500000 | 0.8668333 | 0.170 | 5.9500000 | 0.4919350 | 0.110 | 5.1100000 | 0.6538348 | 0.1500 | 18 | 26 | 20 | 19 | -0.347765189 | Viable | ||||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Harp_h1 | 328.2000000 | 29.9000000 | 440.5000000 | 35.3000000 | 611.5000000 | 79.6000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | 0.273675987 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Harp_h2 | 1029.7000000 | 41.5000000 | 861.4000000 | 40.4000000 | 750.3000000 | 44.8000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.197249077 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Harp_h1/h2 | 0.3200000 | 0.0300000 | 0.5100000 | 0.0300000 | 0.8000000 | 0.1000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | 0.726513411 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Valva_v1 | 608.0000000 | 44.5000000 | 672.7000000 | 70.0000000 | 732.2000000 | 73.9000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.398517284 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Valva_v2 | 891.1000000 | 69.6000000 | 817.7000000 | 74.0000000 | 818.0000000 | 84.8000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.552982374 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Valva_v1/v2 | 0.6800000 | 0.0400000 | 0.8200000 | 0.0800000 | 0.9000000 | 0.0700000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.253080256 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Valva_v3 | 3696.4000000 | 134.5000000 | 3387.1000000 | 125.0000000 | 3314.2000000 | 143.4000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.516710910 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Clasper surface | 269.6000000 | 12.4000000 | 317.4000000 | 17.5000000 | 415.9000000 | 31.4000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.017105678 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Juxta_j1 | 437.0000000 | 33.8000000 | 329.8000000 | 20.5000000 | 281.1000000 | 25.2000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.005219039 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Juxta_j2 | 164.0000000 | 24.0000000 | 194.7000000 | 23.8000000 | 234.6000000 | 21.2000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | -0.133380509 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Juxta_j1/j2 | 0.3800000 | 0.0700000 | 0.5900000 | 0.0900000 | 0.8400000 | 0.1100000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | 0.536967787 | Inviable | ||||||||||
| s14 | Gadenne et al_1997 | table1 | Agrotis_ipsilon_Agrotis_segetum | Agrotis | Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Yes | morphology | Vesica teeth | 17.0000000 | 3.2000000 | 27.6000000 | 5.8000000 | 40.5000000 | 5.1000000 | >100 | >100 | 4 | 4 | 0 | 0 | >100 | >100 | 30 | 24 | 0 | 25 | 0.652269216 | Inviable | ||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Song duration | 11.8400000 | 3.7000000 | 8.3400000 | 3.4500000 | 12.5400000 | 3.3700000 | 12.2600000 | 4.8200000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | -0.631129479 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Phrase duration | 2.0700000 | 0.3200000 | 0.4500000 | 0.0600000 | 0.5600000 | 0.1500000 | 0.1800000 | 0.0400000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | 3.077050352 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Phrase pause | 2.0600000 | 0.5700000 | 1.7400000 | 0.4500000 | 1.9400000 | 0.3900000 | 1.6600000 | 0.5400000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | -0.352294492 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Number of phrases | 3.2000000 | 0.9000000 | 4.7000000 | 1.7000000 | 5.9000000 | 1.6000000 | 7.8000000 | 2.4000000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | 0.687517181 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Syllables per phrase | 32.5000000 | 6.9000000 | 3.4000000 | 1.8000000 | 2.0000000 | 0.7000000 | 5.3000000 | 1.0000000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | 2.108508114 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Pulses per syllable | 6.2000000 | 0.5000000 | 20.2000000 | 16.9000000 | 32.0000000 | 10.5000000 | 3.1000000 | 0.1000000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | 0.382812357 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Syllable period | 60.1000000 | 7.0000000 | 193.8000000 | 156.7000000 | 257.7000000 | 106.8000000 | 29.6000000 | 4.5000000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | 0.406052394 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Pulse period | 8.6000000 | 1.2000000 | 7.9000000 | 0.4000000 | 8.0000000 | 0.5000000 | 8.8000000 | 1.4000000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | -0.649410595 | Viable | ||||||||||||
| s15 | Gottsberger&Mayer_2007 | table1 | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus | Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Yes | sound | Phase-shift in | 85.9000000 | 15.2000000 | 159.6000000 | 28.0000000 | 165.0000000 | 24.4000000 | 131.9000000 | 15.6000000 | 6 | 6 | 3 | 3 | 35 | 19 | 18 | 22 | -0.024260592 | Viable | ||||||||||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | Intra-chirp interval | 52.1000000 | 6.8000000 | 60.4000000 | 6.1000000 | 57.5000000 | 6.5000000 | 66.8000000 | 6.7000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 489 | 807 | 712 | 290 | -0.302005098 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | Intra-trill interval | 31.7000000 | 3.5000000 | 36.7000000 | 5.0000000 | 33.0000000 | 3.8000000 | 41.0000000 | 1.8000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 1840 | 2450 | 2833 | 740 | -0.288578184 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | Inter-trill interval | 160.9000000 | 50.6000000 | 154.0000000 | 38.8000000 | 136.9000000 | 23.6000000 | 122.8000000 | 14.1000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 119 | 693 | 857 | 813 | -0.268599892 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | No. of A-pulses per chirp | 5.9000000 | 0.9000000 | 4.9000000 | 0.7000000 | 4.2000000 | 0.6000000 | 4.8000000 | 0.9000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 100 | 100 | 100 | 100 | -0.367007059 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | No. of B-pulses per trill | 10.7000000 | 5.3000000 | 4.9000000 | 2.0000000 | 4.5000000 | 1.5000000 | 2.0000000 | 0.1000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 147 | 200 | 194 | 250 | 1.898358907 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | No. of B-pulses per phrase | 21.5000000 | 10.4000000 | 19.4000000 | 4.7000000 | 18.4000000 | 4.9000000 | 20.9000000 | 3.0000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 34 | 37 | 28 | 38 | -0.641225209 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | No. of trills per phrase | 2.3000000 | 1.2000000 | 4.8000000 | 1.2000000 | 4.2000000 | 1.0000000 | 9.4000000 | 2.3000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 100 | 100 | 100 | 100 | 1.483570683 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table1 | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus | Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Yes | sound | Phrase repetition rate | 41.7000000 | 18.1000000 | 33.0000000 | 7.0000000 | 37.5000000 | 5.3000000 | 28.5000000 | 2.4000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 10 | 10 | 5 | 102 | 70 | 105 | 77 | -0.098598877 | Viable | ||||
| s16 | Bentrey&Hoy_1972 | table2 | Gryllus_armatus_Gryllus_rubens | Gryllus | Gryllus_armatus | Gryllus_rubens | Gryllus_armatus_Gryllus_rubens | Yes | sound | IPI | 14.6000000 | 1.7000000 | 17.4000000 | 2.0000000 | 19.8000000 | 3.6000000 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 5 | 5 | 5 | 785 | 837 | 439 | -0.215561704 | Inviable | ||||||||
| s16 | Bentrey&Hoy_1972 | table3 | Gryllus_armatus_Gryllus_rubens | Gryllus | Gryllus_armatus | Gryllus_rubens | Gryllus_armatus_Gryllus_rubens | Yes | sound | Chirp length | 3.0095000 | 0.4045488 | 3.0256500 | 1.1668687 | 120.8835000 | 98.8883015 | 4 | 6 | 5 | 1016 | 2275 | 1027 | 5.003474245 | Inviable | ||||||||||||||||
| s16 | Bentrey&Hoy_1972 | table3 | Gryllus_campestris_Gryllus_rubens | Gryllus | Gryllus_campestris | Gryllus_rubens | Gryllus_campestris_Gryllus_rubens | Yes | sound | Chirp length | 6.4650000 | 0.3482456 | 16.1350000 | 11.5580827 | 120.8835000 | 98.8883015 | 4 | 1 | 5 | 1007 | 206 | 1027 | 3.825739945 | Inviable | ||||||||||||||||
| s17 | Magalhaes&Seehausen_2010 | Fig2 | Pundamilia_nyererei_Pundamilia_pundamilia | Pundamilia | Pundamilia_nyererei | Pundamilia_pundamilia | Pundamilia_nyererei_Pundamilia_pundamilia | Yes | morphology | % red cover of body | 26.2272727 | 8.3349310 | 6.8750000 | 6.0194996 | 7.1000000 | 5.1224994 | 2.4500000 | 1.6575584 | 4 | 4 | 3 | 3 | 11 | 24 | 5 | 20 | 2.966712815 | Viable | ||||||||||||
| s18 | Monti et al_2001 | table1 | Spodoptera_descoinsi_Spodoptera_latifascia | Spodoptera | Spodoptera_descoinsi | Spodoptera_latifascia | Spodoptera_descoinsi_Spodoptera_latifascia | Yes | morphology | clasper shape | 977.9300000 | 56.8600000 | 1097.0000000 | 42.5200000 | 1080.8300000 | 66.6200000 | 1117.0000000 | 70.4700000 | <15 | <15 | <15 | <15 | <15 | <15 | <15 | <15 | 30 | 30 | 30 | 30 | -0.480020782 | Viable | ||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_meanduration | 0.0350000 | 0.0120277 | 0.0345000 | 0.0021213 | 0.0381667 | 0.0110529 | 4 | 2 | 6 | -0.551411061 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequstart | 88243.2360000 | 13841.1853318 | 74950.0000000 | 636.3961031 | 72556.1870000 | 12617.0280666 | 4 | 2 | 6 | -0.383334895 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqstart | 86607.4352500 | 13818.4480691 | 73428.1250000 | 667.3320247 | 69262.5583333 | 11355.7727478 | 4 | 2 | 6 | -0.340599520 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfrequstart | 91795.0325000 | 12995.6534046 | 77565.6250000 | 48.6135912 | 78189.1510000 | 12300.4108541 | 4 | 2 | 6 | -0.437719496 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwstart | 5143.7662500 | 2045.3369815 | 4068.7500000 | 662.9126074 | 8864.5688333 | 4581.0818004 | 4 | 2 | 6 | 0.153511885 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropystart | 0.2057500 | 0.0209185 | 0.1930000 | 0.0325269 | 0.2135000 | 0.0154370 | 4 | 2 | 6 | -0.627869204 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequend | 74352.4027500 | 5053.6317335 | 73462.5000000 | 18685.2966929 | 70818.2011667 | 7761.6449976 | 4 | 2 | 6 | -0.609809630 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqend | 72380.2272500 | 5077.6205582 | 71778.1250000 | 18353.8403892 | 68704.3900000 | 7683.0440288 | 4 | 2 | 6 | -0.604541730 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfrequend | 77172.7707500 | 5578.8877045 | 76237.5000000 | 19498.4694912 | 73259.6543333 | 7707.3985467 | 4 | 2 | 6 | -0.604670211 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwend | 4737.1212500 | 658.3624192 | 4400.0000000 | 1131.3708499 | 4521.8338333 | 641.0389018 | 4 | 2 | 6 | -0.613179660 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropyend | 0.2087500 | 0.0171925 | 0.2030000 | 0.0367696 | 0.2091667 | 0.0126399 | 4 | 2 | 6 | -0.681749308 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequcentre | 85535.6927500 | 9125.8577495 | 62084.3750000 | 37454.5623160 | 61978.2051667 | 9275.5460027 | 4 | 2 | 6 | -0.188621563 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqcenter | 80507.1970000 | 8382.7388396 | 69421.8750000 | 21244.1393573 | 61213.2866667 | 9007.7450086 | 4 | 2 | 6 | -0.262814215 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfreqcenter | 90638.4525000 | 8209.6444716 | 95231.2500000 | 2713.5222728 | 71257.7506667 | 7370.4746511 | 4 | 2 | 6 | -0.314270540 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwcentre | 10071.6775000 | 4429.0518530 | 25728.1250000 | 23931.1451258 | 9988.7530000 | 6829.4344123 | 4 | 2 | 6 | -0.672086435 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropycentre | 0.2252500 | 0.0343256 | 0.1705000 | 0.0912168 | 0.2091667 | 0.0341433 | 4 | 2 | 6 | -0.570718267 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequmax | 80739.4265000 | 5303.4742393 | 82650.0000000 | 3676.9552622 | 68118.2983333 | 9187.5342106 | 4 | 2 | 6 | -0.423003987 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqmax | 79202.7922500 | 5361.1342413 | 81371.8750000 | 2930.0737245 | 66305.2156667 | 9378.6307948 | 4 | 2 | 6 | -0.411048644 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfrequmax | 84045.2057500 | 4837.5806504 | 85321.8750000 | 3707.8911838 | 72361.4995000 | 9750.2056443 | 4 | 2 | 6 | -0.454272527 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwmax | 4802.3810000 | 1332.0316481 | 3918.7500000 | 733.6232855 | 6007.6146667 | 2379.1891824 | 4 | 2 | 6 | -0.339929894 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropymax | 0.1950000 | 0.0107393 | 0.1925000 | 0.0162635 | 0.2043333 | 0.0116390 | 4 | 2 | 6 | -0.612808308 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_FAC2_1 | 1.9098486 | 1.8018551 | 0.4389270 | 1.0762664 | 0.4527336 | 1.2603555 | 4 | 2 | 6 | 1.532358568 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_FAC1_3 | 1.4977934 | 0.9941282 | 1.3129292 | 0.3134363 | 3.8618017 | 3.4780153 | 4 | 2 | 6 | 0.774030955 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_FAC1_4 | 0.4874387 | 1.1538739 | -0.6730174 | 2.7297053 | 0.5559659 | 0.6476944 | 4 | 2 | 6 | -0.482209844 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_meanduration | 0.0247500 | 0.0116154 | 0.0330000 | 0.0431429 | 0.0216597 | 4 | 1 | 7 | 0.171093472 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_peakfrequstart | 76228.5715000 | 13271.3152368 | 75540.0000000 | 54580.9524286 | 7151.3235607 | 4 | 1 | 7 | -0.170291706 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_minfreqstart | 74332.1427500 | 12615.6156919 | 74460.0000000 | 53274.7618571 | 6927.0360415 | 4 | 1 | 7 | -0.171786727 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_maxfrequstart | 78808.3332500 | 12749.5641588 | 78860.0000000 | 57440.0001429 | 7073.5971258 | 4 | 1 | 7 | -0.197667775 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_bandwstart | 4411.9050000 | 626.2642551 | 4340.0000000 | 4110.0000000 | 600.3116768 | 4 | 1 | 7 | -0.575640926 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_entropystart | 0.2115000 | 0.0157586 | 0.2210000 | 0.2022857 | 0.0233503 | 4 | 1 | 7 | -0.616211094 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_peakfrequend | 75334.5240000 | 11042.2134461 | 79060.0000000 | 58608.0951429 | 6273.4698049 | 4 | 1 | 7 | -0.298111985 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_minfreqend | 74025.0000000 | 11169.4150847 | 78160.0000000 | 57369.0475714 | 6615.9479668 | 4 | 1 | 7 | -0.292209258 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_maxfrequend | 77971.4285000 | 11022.6693431 | 82940.0000000 | 61825.7142857 | 6094.1748961 | 4 | 1 | 7 | -0.327442700 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_bandwend | 3878.5715000 | 836.3032833 | 4740.0000000 | 4394.2855714 | 1103.0087703 | 4 | 1 | 7 | -0.492536481 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_entropyend | 0.1935000 | 0.0287112 | 0.2140000 | 0.2121429 | 0.0128508 | 4 | 1 | 7 | -0.543143026 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_peakfrequcentre | 84771.4285000 | 11250.2360323 | 90260.0000000 | 65132.8571429 | 6639.2899236 | 4 | 1 | 7 | -0.278914487 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_minfreqcenter | 82939.2857500 | 11362.3125349 | 89020.0000000 | 63461.4287143 | 7232.0898611 | 4 | 1 | 7 | -0.272526799 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_maxfreqcenter | 87214.2857500 | 11278.1558979 | 92820.0000000 | 68880.9524286 | 4518.6205675 | 4 | 1 | 7 | -0.321334970 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_bandwcentre | 4239.2857500 | 426.0486747 | 3800.0000000 | 5404.2857143 | 4186.7844922 | 4 | 1 | 7 | -0.310847230 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_entropycentre | 0.1967500 | 0.0115289 | 0.1930000 | 0.1890000 | 0.0072801 | 4 | 1 | 7 | -0.622922047 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_peakfrequmax | 81715.4760000 | 10451.3541605 | 85680.0000000 | 64371.4285714 | 7250.3760465 | 4 | 1 | 7 | -0.317353264 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_minfreqmax | 80088.0952500 | 10219.5703381 | 84440.0000000 | 62977.1428571 | 7156.8075149 | 4 | 1 | 7 | -0.314608750 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_maxfrequmax | 84541.6665000 | 10896.6653124 | 88540.0000000 | 66872.3810000 | 7126.6805948 | 4 | 1 | 7 | -0.323691569 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_bandwmax | 4390.4762500 | 1075.8253068 | 4080.0000000 | 3884.2855714 | 142.6563258 | 4 | 1 | 7 | -0.496139807 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_entropymax | 0.1997500 | 0.0209821 | 0.1990000 | 0.1897381 | 0.0127089 | 4 | 1 | 7 | -0.605617070 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped Inverted_FAC1_2 | 0.0641031 | 0.7688575 | 0.8763980 | 0.3896661 | 1.0029075 | 4 | 1 | 7 | 2.095052447 | Inviable | ||||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_meanduration | 0.0260000 | 0.0116619 | 0.0376000 | 0.0112827 | 0.0354444 | 0.0097482 | 4 | 5 | 9 | -0.207537553 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequstart | 80479.7617500 | 12733.6564666 | 79085.4762000 | 3447.3265538 | 65840.7107778 | 5732.7881166 | 4 | 5 | 9 | -0.375584876 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqstart | 79164.8807500 | 12512.0095023 | 77460.9522000 | 3600.6844370 | 63745.1970000 | 5888.2100068 | 4 | 5 | 9 | -0.351138435 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfrequstart | 82837.5000000 | 12669.1982077 | 81530.7144000 | 3665.7522184 | 68163.0446667 | 5734.4353543 | 4 | 5 | 9 | -0.384501627 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwstart | 3639.8810000 | 853.3854609 | 4006.1906000 | 651.1667981 | 4360.5252222 | 453.4732575 | 4 | 5 | 9 | -0.406584124 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropystart | 0.1835000 | 0.0233880 | 0.1972000 | 0.0226208 | 0.2200000 | 0.0225278 | 4 | 5 | 9 | -0.405396032 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequend | 79257.1427500 | 12462.3047588 | 80213.0952000 | 4487.0077220 | 66126.1151111 | 6271.9991028 | 4 | 5 | 9 | -0.405825913 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqend | 77598.8095000 | 12226.8021838 | 78693.8096000 | 4816.5305116 | 64482.5417778 | 5952.0201006 | 4 | 5 | 9 | -0.399628204 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfrequend | 81726.7857500 | 12753.0766460 | 83136.1904000 | 4656.4245579 | 69003.3801111 | 5730.3578037 | 4 | 5 | 9 | -0.424166620 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwend | 4083.3335000 | 786.2241599 | 4392.8570000 | 725.5012887 | 4462.8988889 | 468.0169154 | 4 | 5 | 9 | -0.547914084 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropyend | 0.1977500 | 0.0196193 | 0.2048000 | 0.0207292 | 0.2275556 | 0.0441478 | 4 | 5 | 9 | -0.468581058 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequcentre | 74933.3332500 | 10968.5408399 | 71673.8094000 | 3009.5076498 | 59479.1160000 | 5400.4655878 | 4 | 5 | 9 | -0.329059247 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqcenter | 72616.6667500 | 12624.6233365 | 70144.2856000 | 2999.7169555 | 57936.3224444 | 5682.7417545 | 4 | 5 | 9 | -0.336951003 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfreqcenter | 80083.9285000 | 8479.2792799 | 74386.4286000 | 3202.4808429 | 62278.1645556 | 5499.6658025 | 4 | 5 | 9 | -0.297497989 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwcentre | 7431.5477500 | 6279.7280238 | 4214.0476000 | 363.2867758 | 4307.3395556 | 818.6217348 | 4 | 5 | 9 | 0.155263093 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropycentre | 0.1990000 | 0.0053541 | 0.1952000 | 0.0056303 | 0.2087778 | 0.0237106 | 4 | 5 | 9 | -0.610940719 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequmax | 79097.6192500 | 11822.7651521 | 76354.7620000 | 5142.8431679 | 63787.8418889 | 5091.3037702 | 4 | 5 | 9 | -0.353477742 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqmax | 77244.0475000 | 11659.2282489 | 74737.8572000 | 5116.6142024 | 62107.5704444 | 4788.4915589 | 4 | 5 | 9 | -0.348884971 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfrequmax | 81376.1905000 | 11690.8610379 | 78997.6190000 | 5192.4923151 | 66376.4416667 | 5054.8521007 | 4 | 5 | 9 | -0.371005446 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwmax | 4088.6907500 | 268.9063550 | 4235.0000000 | 620.6398924 | 4223.3141111 | 397.7357504 | 4 | 5 | 9 | -0.634923093 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropymax | 0.1930000 | 0.0054772 | 0.1956000 | 0.0098641 | 0.2045556 | 0.0125111 | 4 | 5 | 9 | -0.595254902 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_meanduration | 0.0285000 | 0.0063509 | 0.0276000 | 0.0031305 | 0.0351000 | 0.0073401 | 4 | 5 | 10 | -0.363987192 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequstart | 69339.9377500 | 6362.5905607 | 66031.8856000 | 3929.1367989 | 54773.7979000 | 4196.1911989 | 4 | 5 | 10 | -0.321611224 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqstart | 68133.1495000 | 6257.9552643 | 64796.1706000 | 4082.6666572 | 53275.0191000 | 4213.6700004 | 4 | 5 | 10 | -0.305920148 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfrequstart | 71765.7477500 | 6142.9162222 | 68733.4816000 | 3952.8608450 | 57315.1191000 | 4048.9568152 | 4 | 5 | 10 | -0.338502181 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwstart | 3589.9292500 | 176.6865194 | 3887.3656000 | 476.5605623 | 3994.0754000 | 380.9553189 | 4 | 5 | 10 | -0.520505343 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropystart | 0.1867500 | 0.0066521 | 0.1906000 | 0.0158524 | 0.2022000 | 0.0143279 | 4 | 5 | 10 | -0.562390082 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequend | 80755.2170000 | 7130.3793512 | 78555.2896000 | 5371.4415642 | 66458.1019000 | 4740.6017565 | 4 | 5 | 10 | -0.384697922 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqend | 79211.8740000 | 7021.7575623 | 76984.6620000 | 5382.1778808 | 64829.2506000 | 4552.7376009 | 4 | 5 | 10 | -0.376198029 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfrequend | 83357.7775000 | 7015.1036046 | 81298.9990000 | 5595.8063179 | 69166.6400000 | 4648.9534970 | 4 | 5 | 10 | -0.397370659 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwend | 4100.5467500 | 191.5042202 | 4258.5490000 | 361.0614413 | 4288.1656000 | 429.1656564 | 4 | 5 | 10 | -0.615910971 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropyend | 0.1982500 | 0.0082614 | 0.2010000 | 0.0125499 | 0.2114000 | 0.0152184 | 4 | 5 | 10 | -0.585899621 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequcentre | 74106.6220000 | 7044.5184840 | 73891.5844000 | 5092.2358605 | 61283.1031000 | 3445.7123977 | 4 | 5 | 10 | -0.392168430 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqcenter | 72668.0320000 | 6267.5002741 | 71986.6968000 | 4961.9873412 | 59578.0253000 | 3392.0761680 | 4 | 5 | 10 | -0.378900511 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfreqcenter | 78169.5640000 | 5725.8571623 | 76532.0698000 | 5240.1316395 | 63854.9729000 | 3631.4868123 | 4 | 5 | 10 | -0.373276772 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwcentre | 5460.9155000 | 1647.2090987 | 4501.4542000 | 316.2865754 | 4241.4512000 | 460.9409794 | 4 | 5 | 10 | -0.295577798 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropycentre | 0.2055000 | 0.0124499 | 0.2020000 | 0.0077782 | 0.2016000 | 0.0121033 | 4 | 5 | 10 | -0.655308316 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequmax | 77823.1835000 | 5399.8126400 | 75780.1022000 | 5415.3370781 | 63366.8131000 | 3563.3066643 | 4 | 5 | 10 | -0.368296771 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqmax | 75957.8212500 | 5436.7383936 | 74190.8194000 | 5410.9982527 | 61786.7749000 | 3572.0160930 | 4 | 5 | 10 | -0.366772281 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfrequmax | 80373.0087500 | 5426.3663453 | 78304.8670000 | 5489.1644712 | 65856.5116000 | 3558.8589776 | 4 | 5 | 10 | -0.377998932 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwmax | 4377.1182500 | 458.3771312 | 4075.2264000 | 103.6274969 | 4038.5590000 | 95.8375937 | 4 | 5 | 10 | -0.560824944 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropymax | 0.1977500 | 0.0121758 | 0.1922000 | 0.0046583 | 0.1960000 | 0.0109747 | 4 | 5 | 10 | -0.671129264 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_meanduration | 0.0267500 | 0.0025000 | 0.0268000 | 0.0038987 | 0.0344000 | 0.0176522 | 4 | 5 | 10 | -0.297408999 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequstart | 92946.9280000 | 15749.6397527 | 86481.4286000 | 5235.4356333 | 74913.1969000 | 7786.3062625 | 4 | 5 | 10 | -0.352586813 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqstart | 91265.4315000 | 15635.4785025 | 85101.1546000 | 5126.9975016 | 72762.6818000 | 8026.5298721 | 4 | 5 | 10 | -0.335843653 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfrequstart | 95250.4370000 | 15611.6348854 | 88932.3952000 | 5456.0298891 | 77138.1818000 | 7532.7019771 | 4 | 5 | 10 | -0.359960658 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwstart | 3938.6365000 | 407.4663826 | 3770.7360000 | 571.0050616 | 4327.9697000 | 1129.8378295 | 4 | 5 | 10 | -0.539628821 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropystart | 0.1940000 | 0.0162686 | 0.1890000 | 0.0151162 | 0.2120000 | 0.0258844 | 4 | 5 | 10 | -0.548155483 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequend | 80321.3132500 | 13264.1601325 | 76316.7246000 | 3882.0213419 | 66018.2273000 | 6019.6800990 | 4 | 5 | 10 | -0.382767555 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqend | 78858.0547500 | 13220.5889301 | 74963.2754000 | 4014.2011731 | 64436.2727000 | 5953.7214161 | 4 | 5 | 10 | -0.373728310 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfrequend | 83015.5642500 | 13098.4743972 | 79279.4950000 | 3972.5831489 | 68731.5455000 | 5833.3199357 | 4 | 5 | 10 | -0.393987321 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwend | 4104.4317500 | 319.2320090 | 4266.2626000 | 160.5837063 | 4239.1515000 | 376.5780209 | 4 | 5 | 10 | -0.635076378 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropyend | 0.1957500 | 0.0127377 | 0.1992000 | 0.0032711 | 0.2177000 | 0.0218228 | 4 | 5 | 10 | -0.521121428 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequcentre | 85783.7080000 | 13616.0068270 | 80867.3596000 | 5707.2122076 | 65722.3939000 | 5496.8625711 | 4 | 5 | 10 | -0.274509367 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqcenter | 84079.9412500 | 13437.8479318 | 79064.9062000 | 5758.6121464 | 64018.1667000 | 5500.6339410 | 4 | 5 | 10 | -0.264941612 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfreqcenter | 88618.9480000 | 13627.6886529 | 83648.0088000 | 5894.4675948 | 68351.4394000 | 5563.6384045 | 4 | 5 | 10 | -0.284838893 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwcentre | 4503.1745000 | 390.7571406 | 4530.2886000 | 282.0421900 | 4283.8030000 | 426.1499071 | 4 | 5 | 10 | -0.607897412 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropycentre | 0.2027500 | 0.0092871 | 0.2036000 | 0.0094499 | 0.2030000 | 0.0185053 | 4 | 5 | 10 | -0.682922581 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequmax | 86744.4337500 | 13629.4541780 | 80625.3968000 | 5025.9533091 | 66385.7122000 | 5290.0326237 | 4 | 5 | 10 | -0.272822757 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqmax | 84959.1095000 | 13504.3981075 | 79065.7430000 | 5016.1123507 | 64919.6969000 | 5355.5238651 | 4 | 5 | 10 | -0.270459052 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfrequmax | 89437.1925000 | 13622.5958524 | 83594.9494000 | 5076.7807417 | 68886.5151000 | 5319.5671675 | 4 | 5 | 10 | -0.282693138 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwmax | 4434.5437500 | 323.8048150 | 4497.0850000 | 312.9490788 | 3952.0908000 | 142.0981267 | 4 | 5 | 10 | -0.507412501 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropymax | 0.1990000 | 0.0115181 | 0.2004000 | 0.0069498 | 0.1904000 | 0.0112270 | 4 | 5 | 10 | -0.616775096 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_FAC2_1 | 2.0886949 | 1.1136315 | 1.5887848 | 0.3969399 | 0.8389634 | 0.6739541 | 4 | 5 | 10 | 0.720101036 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_meanduration | 0.0265000 | 0.0106615 | 0.0292000 | 0.0046043 | 0.0490000 | 0.0188797 | 4 | 5 | 10 | 0.261944974 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequstart | 69169.0910000 | 9324.4840586 | 72231.8094000 | 4372.7725516 | 58622.4861000 | 4177.5985187 | 4 | 5 | 10 | -0.430005096 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqstart | 67626.3637500 | 9012.4152325 | 70881.4066000 | 4153.6520615 | 57128.3611000 | 4289.6169090 | 4 | 5 | 10 | -0.424981568 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfrequstart | 71406.1362500 | 8903.2731162 | 74543.2748000 | 4406.7481866 | 60936.9862000 | 4080.3940669 | 4 | 5 | 10 | -0.440621038 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwstart | 3756.6667500 | 157.6212831 | 3607.1428000 | 355.7839191 | 3761.9722000 | 634.3257829 | 4 | 5 | 10 | -0.682646883 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropystart | 0.1860000 | 0.0040825 | 0.1856000 | 0.0107842 | 0.2004000 | 0.0206839 | 4 | 5 | 10 | -0.569964796 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequend | 69355.7577500 | 7543.9312104 | 73531.9192000 | 2979.5723937 | 59648.3194000 | 4627.5588424 | 4 | 5 | 10 | -0.452573981 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqend | 67679.9242500 | 7694.1337933 | 72042.2784000 | 2956.8202038 | 58407.7222000 | 4528.6555691 | 4 | 5 | 10 | -0.457875137 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfrequend | 72289.7727500 | 7207.9161126 | 75923.6484000 | 3046.3029627 | 62455.1249000 | 4470.2205884 | 4 | 5 | 10 | -0.459580398 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwend | 4533.4850000 | 626.2282663 | 3833.2748000 | 260.5736903 | 4000.4722000 | 271.3453083 | 4 | 5 | 10 | -0.492166034 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropyend | 0.2062500 | 0.0107819 | 0.1920000 | 0.0083964 | 0.2032000 | 0.0143201 | 4 | 5 | 10 | -0.661873231 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequcentre | 68660.6817500 | 7663.4948537 | 72021.6116000 | 3615.6610863 | 59662.3749000 | 4149.6050515 | 4 | 5 | 10 | -0.468451168 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqcenter | 67142.2727500 | 7703.6470736 | 70267.8316000 | 3511.0302351 | 58124.8194000 | 4183.0463575 | 4 | 5 | 10 | -0.462681393 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfreqcenter | 71632.0455000 | 7409.1498507 | 74567.8900000 | 3563.3700105 | 62026.3194000 | 4196.2300849 | 4 | 5 | 10 | -0.463046975 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwcentre | 4444.6212500 | 673.9336392 | 4261.2308000 | 595.5497093 | 3878.5416000 | 177.2936857 | 4 | 5 | 10 | -0.474981416 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropycentre | 0.1990000 | 0.0134412 | 0.2014000 | 0.0173436 | 0.1961000 | 0.0112195 | 4 | 5 | 10 | -0.662209346 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequmax | 68920.4545000 | 7723.1945601 | 73048.2564000 | 3656.6152656 | 60169.5138000 | 4456.3801999 | 4 | 5 | 10 | -0.475671847 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqmax | 67252.9545000 | 7730.3144970 | 71570.0880000 | 3681.7389827 | 58666.8750000 | 4456.7669770 | 4 | 5 | 10 | -0.474441949 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfrequmax | 71630.7577500 | 7551.5296524 | 75444.2492000 | 3592.5206245 | 62560.9861000 | 4458.4226262 | 4 | 5 | 10 | -0.476294880 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwmax | 4330.1515000 | 407.1581403 | 3852.3444000 | 191.5057684 | 3868.9027000 | 174.3981447 | 4 | 5 | 10 | -0.511337687 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.1 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropymax | 0.1997500 | 0.0156498 | 0.1892000 | 0.0093381 | 0.1915000 | 0.0102008 | 4 | 5 | 10 | -0.619853979 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_meanduration | 0.0350000 | 0.0120277 | 0.0381667 | 0.0066458 | 0.0425000 | 0.0275772 | 4 | 6 | 2 | -0.385768178 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequstart | 88243.2360000 | 13841.1853318 | 73305.6713333 | 4769.9467214 | 66238.6365000 | 12885.4138334 | 4 | 6 | 2 | -0.243020520 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqstart | 86607.4352500 | 13818.4480691 | 71600.6366667 | 4899.4887991 | 65145.4545000 | 13795.0105409 | 4 | 6 | 2 | -0.246208851 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfrequstart | 91795.0325000 | 12995.6534046 | 75512.6351667 | 4620.4944652 | 69802.2725000 | 13785.3684328 | 4 | 6 | 2 | -0.262953915 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropystart | 0.2057500 | 0.0209185 | 0.1780000 | 0.0144914 | 0.2025000 | 0.0035355 | 4 | 6 | 2 | -0.660296545 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequend | 74352.4027500 | 5053.6317335 | 77592.3225000 | 10448.6713702 | 61843.1820000 | 5666.4963576 | 4 | 6 | 2 | -0.401081351 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqend | 72380.2272500 | 5077.6205582 | 75370.5631667 | 9716.7533828 | 59870.4545000 | 5062.2417932 | 4 | 6 | 2 | -0.392554591 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfrequend | 77172.7707500 | 5578.8877045 | 79990.5866667 | 10512.6172950 | 65202.2725000 | 4451.5589212 | 4 | 6 | 2 | -0.425204686 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwend | 4737.1212500 | 658.3624192 | 4577.4498333 | 867.7424719 | 5268.1820000 | 662.1093341 | 4 | 6 | 2 | -0.521158274 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropyend | 0.2087500 | 0.0171925 | 0.1993333 | 0.0194182 | 0.2220000 | 0.0028284 | 4 | 6 | 2 | -0.590032462 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequcentre | 85535.6927500 | 9125.8577495 | 77685.0115000 | 5235.8011518 | 63693.1820000 | 40385.4393138 | 4 | 6 | 2 | -0.230662785 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqcenter | 80507.1970000 | 8382.7388396 | 75118.2870000 | 4715.1801229 | 67679.5455000 | 33050.8137127 | 4 | 6 | 2 | -0.417487596 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfreqcenter | 90638.4525000 | 8209.6444716 | 85149.2475000 | 7210.7901272 | 96740.9090000 | 2886.2811874 | 4 | 6 | 2 | -0.584460074 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwcentre | 10071.6775000 | 4429.0518530 | 9980.9800000 | 5931.1228099 | 29015.9090000 | 35872.8125297 | 4 | 6 | 2 | 0.944963973 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropycentre | 0.2252500 | 0.0343256 | 0.2113333 | 0.0233467 | 0.1615000 | 0.0544472 | 4 | 6 | 2 | -0.172364334 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_peakfrequmax | 80739.4265000 | 5303.4742393 | 78668.1135000 | 5702.1882220 | 70011.3635000 | 12075.4555425 | 4 | 6 | 2 | -0.465225358 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_minfreqmax | 79202.7922500 | 5361.1342413 | 76413.0593333 | 6057.2386935 | 68222.7275000 | 12554.3591664 | 4 | 6 | 2 | -0.454960469 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_maxfrequmax | 84045.2057500 | 4837.5806504 | 81311.1883333 | 5595.5060910 | 72500.0000000 | 12020.8152802 | 4 | 6 | 2 | -0.457217796 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_bandwmax | 4802.3810000 | 1332.0316481 | 4874.2476667 | 1278.9380970 | 4263.6365000 | 514.2596701 | 4 | 6 | 2 | -0.501544967 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_entropymax | 0.1950000 | 0.0107393 | 0.1935000 | 0.0051284 | 0.1985000 | 0.0049497 | 4 | 6 | 2 | -0.657419957 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_FAC2_1 | 1.9098486 | 1.8018551 | 0.0221902 | 0.5561929 | 0.0761514 | 1.0631389 | 4 | 6 | 2 | 4.278011534 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Complex 2_FAC1_3 | 1.4977934 | 0.9941282 | 0.9259184 | 1.1546713 | 2.3807529 | 3.0564478 | 4 | 6 | 2 | 0.028976672 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_meanduration | 0.0260000 | 0.0116619 | 0.0317143 | 0.0024300 | 0.0331000 | 0.0117988 | 4 | 7 | 10 | -0.312943144 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequstart | 80479.7617500 | 12733.6564666 | 77879.0675714 | 4602.9590631 | 69479.9405000 | 11645.2813142 | 4 | 7 | 10 | -0.458450927 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqstart | 79164.8807500 | 12512.0095023 | 76067.6807143 | 4701.9548514 | 67727.2024000 | 11534.8704547 | 4 | 7 | 10 | -0.444469588 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfrequstart | 82837.5000000 | 12669.1982077 | 80050.3137143 | 4775.5257050 | 71814.9405000 | 11778.6104092 | 4 | 7 | 10 | -0.464888239 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwstart | 3639.8810000 | 853.3854609 | 3939.8922857 | 433.8022877 | 4029.5834000 | 680.4207605 | 4 | 7 | 10 | -0.528156800 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropystart | 0.1835000 | 0.0233880 | 0.1830000 | 0.0123018 | 0.1937000 | 0.0344353 | 4 | 7 | 10 | -0.601498400 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequend | 79257.1427500 | 12462.3047588 | 81017.5178571 | 6016.4509795 | 71188.0953000 | 14556.4775881 | 4 | 7 | 10 | -0.519438990 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqend | 77598.8095000 | 12226.8021838 | 79011.1092857 | 5757.6575979 | 69773.2143000 | 14066.5281763 | 4 | 7 | 10 | -0.521087133 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfrequend | 81726.7857500 | 12753.0766460 | 83337.7177143 | 6083.9637972 | 73858.9881000 | 14204.2004101 | 4 | 7 | 10 | -0.528908317 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwend | 4083.3335000 | 786.2241599 | 4277.4301429 | 683.3155854 | 4027.2619000 | 688.5740425 | 4 | 7 | 10 | -0.663523377 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropyend | 0.1977500 | 0.0196193 | 0.1867143 | 0.0146483 | 0.2027000 | 0.0311450 | 4 | 7 | 10 | -0.646739879 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequcentre | 74933.3332500 | 10968.5408399 | 70775.6034286 | 3871.4823805 | 65815.0594000 | 11251.9001331 | 4 | 7 | 10 | -0.484970523 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqcenter | 72616.6667500 | 12624.6233365 | 69188.4248571 | 3898.1055085 | 64420.1786000 | 11199.3646633 | 4 | 7 | 10 | -0.500346412 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfreqcenter | 80083.9285000 | 8479.2792799 | 73324.4728571 | 4012.8599083 | 68504.0477000 | 10935.3646195 | 4 | 7 | 10 | -0.444257818 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwcentre | 7431.5477500 | 6279.7280238 | 4103.1570000 | 295.9083431 | 4036.2500000 | 473.3382165 | 4 | 7 | 10 | 0.255387341 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropycentre | 0.1990000 | 0.0053541 | 0.1841429 | 0.0055806 | 0.1986000 | 0.0183012 | 4 | 7 | 10 | -0.681721500 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_peakfrequmax | 79097.6192500 | 11822.7651521 | 74312.5190000 | 3267.9708291 | 69627.7381000 | 11480.9355266 | 4 | 7 | 10 | -0.488405934 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_minfreqmax | 77244.0475000 | 11659.2282489 | 72715.0254286 | 3256.1742969 | 68056.9047000 | 11431.6737156 | 4 | 7 | 10 | -0.489783064 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_maxfrequmax | 81376.1905000 | 11690.8610379 | 76883.3712857 | 3125.7538591 | 72044.1071000 | 11604.9769219 | 4 | 7 | 10 | -0.497209421 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_bandwmax | 4088.6907500 | 268.9063550 | 4128.2158571 | 202.3831040 | 3950.5357000 | 342.2510779 | 4 | 7 | 10 | -0.631876020 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | U Shaped_entropymax | 0.1930000 | 0.0054772 | 0.1828571 | 0.0047759 | 0.1946000 | 0.0209242 | 4 | 7 | 10 | -0.672104203 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_meanduration | 0.0285000 | 0.0063509 | 0.0300000 | 0.0036968 | 0.0274000 | 0.0056214 | 4 | 7 | 10 | -0.624193998 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequstart | 69339.9377500 | 6362.5905607 | 68451.6210000 | 2875.9266220 | 62809.4284000 | 7177.9005225 | 4 | 7 | 10 | -0.532463639 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqstart | 68133.1495000 | 6257.9552643 | 67045.1720000 | 2746.4746641 | 61456.5655000 | 7116.6283122 | 4 | 7 | 10 | -0.525967783 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfrequstart | 71765.7477500 | 6142.9162222 | 70854.8157143 | 2947.6970887 | 65648.8367000 | 7248.4481801 | 4 | 7 | 10 | -0.547602042 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwstart | 3589.9292500 | 176.6865194 | 3775.6927143 | 345.8361665 | 4137.3085000 | 395.3846050 | 4 | 7 | 10 | -0.466236524 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropystart | 0.1867500 | 0.0066521 | 0.1787143 | 0.0085968 | 0.2046000 | 0.0203372 | 4 | 7 | 10 | -0.544215608 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequend | 80755.2170000 | 7130.3793512 | 84010.6845714 | 7798.9189014 | 72124.2288000 | 8826.0091312 | 4 | 7 | 10 | -0.510720144 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqend | 79211.8740000 | 7021.7575623 | 82066.5910000 | 7545.2449096 | 70563.0160000 | 8746.6720876 | 4 | 7 | 10 | -0.506734664 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfrequend | 83357.7775000 | 7015.1036046 | 86438.1841429 | 7823.3391056 | 74765.7748000 | 8842.4361599 | 4 | 7 | 10 | -0.517267585 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwend | 4100.5467500 | 191.5042202 | 4319.4790000 | 348.1816172 | 4153.6997000 | 370.0441849 | 4 | 7 | 10 | -0.664983337 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropyend | 0.1982500 | 0.0082614 | 0.1891429 | 0.0079042 | 0.2075000 | 0.0245956 | 4 | 7 | 10 | -0.614580556 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequcentre | 74106.6220000 | 7044.5184840 | 72854.8815714 | 4026.8954597 | 70103.3933000 | 7576.5492005 | 4 | 7 | 10 | -0.599283815 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqcenter | 72668.0320000 | 6267.5002741 | 70949.8125714 | 3823.1241531 | 68409.5260000 | 7641.2047793 | 4 | 7 | 10 | -0.591804601 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfreqcenter | 78169.5640000 | 5725.8571623 | 75534.1190000 | 4229.4249557 | 72564.2708000 | 7651.9750334 | 4 | 7 | 10 | -0.570212770 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwcentre | 5460.9155000 | 1647.2090987 | 4543.9688571 | 473.1665573 | 4121.6782000 | 165.3409998 | 4 | 7 | 10 | -0.251456699 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropycentre | 0.2055000 | 0.0124499 | 0.1920000 | 0.0080416 | 0.1996000 | 0.0182160 | 4 | 7 | 10 | -0.639951570 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_peakfrequmax | 77823.1835000 | 5399.8126400 | 75226.5405714 | 3353.0057009 | 70628.7162000 | 7823.2305707 | 4 | 7 | 10 | -0.535410602 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_minfreqmax | 75957.8212500 | 5436.7383936 | 73565.3675714 | 3374.9969637 | 69142.0198000 | 7795.7957098 | 4 | 7 | 10 | -0.540011404 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_maxfrequmax | 80373.0087500 | 5426.3663453 | 77772.7288571 | 3462.0600997 | 73062.2498000 | 7849.4368575 | 4 | 7 | 10 | -0.537930443 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_bandwmax | 4377.1182500 | 458.3771312 | 4169.4832857 | 128.2774426 | 3893.3592000 | 143.5696943 | 4 | 7 | 10 | -0.504427057 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_entropymax | 0.1977500 | 0.0121758 | 0.1842857 | 0.0035923 | 0.1923000 | 0.0179075 | 4 | 7 | 10 | -0.641774811 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Upsweep_FAC1_1 | 1.0730254 | 0.6319905 | 1.1317147 | 0.6998538 | 0.3490031 | 0.8701206 | 4 | 7 | 10 | 1.045142241 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_meanduration | 0.0267500 | 0.0025000 | 0.0188571 | 0.0048107 | 0.0342000 | 0.0090529 | 4 | 7 | 10 | -0.306390184 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequstart | 92946.9280000 | 15749.6397527 | 83172.8458571 | 6591.5365310 | 74608.2984000 | 9917.5386682 | 4 | 7 | 10 | -0.346305090 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqstart | 91265.4315000 | 15635.4785025 | 81643.7951429 | 6791.6267628 | 73061.0328000 | 9813.6525389 | 4 | 7 | 10 | -0.342146350 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfrequstart | 95250.4370000 | 15611.6348854 | 85390.5380000 | 6887.5176862 | 76974.2429000 | 9878.5598701 | 4 | 7 | 10 | -0.356683699 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwstart | 3938.6365000 | 407.4663826 | 3697.4025714 | 538.0658783 | 3869.6487000 | 469.9430250 | 4 | 7 | 10 | -0.657602743 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropystart | 0.1940000 | 0.0162686 | 0.1780000 | 0.0167929 | 0.1974000 | 0.0245728 | 4 | 7 | 10 | -0.658060052 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequend | 80321.3132500 | 13264.1601325 | 75947.5367143 | 5476.7229011 | 66474.0521000 | 8898.1472848 | 4 | 7 | 10 | -0.393365840 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqend | 78858.0547500 | 13220.5889301 | 74498.0982857 | 5701.1827887 | 64969.0043000 | 8816.5109640 | 4 | 7 | 10 | -0.386410243 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfrequend | 83015.5642500 | 13098.4743972 | 78965.3731429 | 6242.7990745 | 69391.8936000 | 8906.2629017 | 4 | 7 | 10 | -0.408715036 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwend | 4104.4317500 | 319.2320090 | 4429.1331429 | 1120.8845735 | 4364.9190000 | 441.7075033 | 4 | 7 | 10 | -0.590044307 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropyend | 0.1957500 | 0.0127377 | 0.2000000 | 0.0380701 | 0.2101000 | 0.0247855 | 4 | 7 | 10 | -0.575853895 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequcentre | 85783.7080000 | 13616.0068270 | 77128.8085714 | 6248.6809489 | 69161.7468000 | 8267.9554886 | 4 | 7 | 10 | -0.353075706 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqcenter | 84079.9412500 | 13437.8479318 | 75565.8524286 | 6160.4471028 | 67649.9982000 | 8204.8806697 | 4 | 7 | 10 | -0.349934308 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfreqcenter | 88618.9480000 | 13627.6886529 | 79822.0831429 | 6243.4272218 | 71784.9698000 | 8317.2169852 | 4 | 7 | 10 | -0.360331315 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwcentre | 4503.1745000 | 390.7571406 | 4201.2110000 | 195.3805990 | 4089.9495000 | 253.4433097 | 4 | 7 | 10 | -0.536569806 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropycentre | 0.2027500 | 0.0092871 | 0.1898571 | 0.0052418 | 0.1996000 | 0.0182221 | 4 | 7 | 10 | -0.660702597 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_peakfrequmax | 86744.4337500 | 13629.4541780 | 77734.9567143 | 5425.7783009 | 70111.6135000 | 8434.1731776 | 4 | 7 | 10 | -0.356931593 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_minfreqmax | 84959.1095000 | 13504.3981075 | 76064.7290000 | 5169.3925630 | 68609.6506000 | 8448.2002277 | 4 | 7 | 10 | -0.355608394 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_maxfrequmax | 89437.1925000 | 13622.5958524 | 80257.0294286 | 5557.8365151 | 72587.0407000 | 8452.0702898 | 4 | 7 | 10 | -0.363289184 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_bandwmax | 4434.5437500 | 323.8048150 | 4156.9417143 | 513.0828757 | 3949.5721000 | 94.2193667 | 4 | 7 | 10 | -0.506430562 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_entropymax | 0.1990000 | 0.0115181 | 0.1850000 | 0.0129615 | 0.1937000 | 0.0171727 | 4 | 7 | 10 | -0.643242225 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_FAC2_1 | 2.0886949 | 1.1136315 | 1.2441253 | 0.5051707 | 0.7618315 | 0.6656533 | 4 | 7 | 10 | 0.868647093 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Frequ Modulated Downsweep_FAC1_2 | 0.0126201 | 0.5416632 | 0.4169407 | 0.6986566 | 0.2071921 | 1.1713853 | 4 | 7 | 10 | 3.625400558 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_meanduration | 0.0265000 | 0.0106615 | 0.0270000 | 0.0071880 | 0.0310000 | 0.0078031 | 4 | 7 | 10 | -0.443241065 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequstart | 69169.0910000 | 9324.4840586 | 63660.0515301 | 25465.5528228 | 66991.5600000 | 8194.9523706 | 4 | 7 | 10 | -0.635551346 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqstart | 67626.3637500 | 9012.4152325 | 62489.4885774 | 24999.7828272 | 65844.5850000 | 8115.5487360 | 4 | 7 | 10 | -0.643694361 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfrequstart | 71406.1362500 | 8903.2731162 | 65671.9343991 | 26271.1332349 | 69454.7217000 | 8199.8533693 | 4 | 7 | 10 | -0.642141704 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwstart | 3756.6667500 | 157.6212831 | 3144.0522479 | 1261.7363913 | 3561.5091000 | 715.7415371 | 4 | 7 | 10 | -0.602650831 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropystart | 0.1860000 | 0.0040825 | 0.4902256 | 1.2484114 | 0.1876000 | 0.0312915 | 4 | 7 | 10 | -0.671627672 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequend | 69355.7577500 | 7543.9312104 | 64144.7233062 | 25832.1297413 | 67522.4211000 | 8547.3791192 | 4 | 7 | 10 | -0.643557662 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqend | 67679.9242500 | 7694.1337933 | 62735.7946403 | 25225.2407203 | 66277.7084000 | 8406.3181878 | 4 | 7 | 10 | -0.652573598 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfrequend | 72289.7727500 | 7207.9161126 | 66204.7049575 | 26631.2674949 | 70072.8344000 | 8466.4939927 | 4 | 7 | 10 | -0.636845155 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwend | 4533.4850000 | 626.2282663 | 3436.0465660 | 1511.7179335 | 3743.4125000 | 411.9136739 | 4 | 7 | 10 | -0.389869426 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropyend | 0.2062500 | 0.0107819 | 0.4967598 | 1.2467428 | 0.1918000 | 0.0224935 | 4 | 7 | 10 | -0.572941878 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequcentre | 68660.6817500 | 7663.4948537 | 63366.9744458 | 25500.1528586 | 68809.8620000 | 8155.6970594 | 4 | 7 | 10 | -0.681477695 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqcenter | 67142.2727500 | 7703.6470736 | 61936.1854557 | 24915.9881117 | 67349.2843000 | 8118.6006467 | 4 | 7 | 10 | -0.680079017 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfreqcenter | 71632.0455000 | 7409.1498507 | 65518.2060007 | 26340.0697982 | 71150.9535000 | 8127.7818884 | 4 | 7 | 10 | -0.674441053 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwcentre | 4444.6212500 | 673.9336392 | 3594.6877006 | 1395.2722123 | 3784.8907000 | 131.2380772 | 4 | 7 | 10 | -0.437333862 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropycentre | 0.1990000 | 0.0134412 | 0.4995162 | 1.2458998 | 0.1901000 | 0.0166897 | 4 | 7 | 10 | -0.614346287 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_peakfrequmax | 68920.4545000 | 7723.1945601 | 63978.4102444 | 25657.4176630 | 68911.7817000 | 8343.3168913 | 4 | 7 | 10 | -0.684626797 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_minfreqmax | 67252.9545000 | 7730.3144970 | 62686.6273322 | 25145.8487055 | 67480.7775000 | 8341.8147142 | 4 | 7 | 10 | -0.679611707 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_maxfrequmax | 71630.7577500 | 7551.5296524 | 66036.0423216 | 26494.0638733 | 71293.2103000 | 8344.5429680 | 4 | 7 | 10 | -0.677545229 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_bandwmax | 4330.1515000 | 407.1581403 | 3352.2286112 | 1328.7828084 | 3787.5374000 | 147.5658605 | 4 | 7 | 10 | -0.478599456 | Inviable | |||||||||||||||||||
| s19 | Musolf et al_2015 | S2 | musculus.3 | Mus | Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | Yes | sound | Constant Modulated_entropymax | 0.1997500 | 0.0156498 | 0.4928359 | 1.2476990 | 0.1878000 | 0.0161644 | 4 | 7 | 10 | -0.589802962 | Inviable | |||||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_montana_Drosophila_virilis | Drosophila | Drosophila_montana | Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Yes | sound | PI | 20.7000000 | 2.7100000 | 20.0000000 | 2.7400000 | 19.7000000 | 1.2500000 | 10 | 10 | 10 | 5 | 10 | -0.608554123 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_montana_Drosophila_virilis | Drosophila | Drosophila_montana | Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Yes | sound | IPI | 35.7000000 | 2.1100000 | 30.2000000 | 3.2700000 | 19.7000000 | 1.2500000 | 10 | 10 | 10 | 5 | 10 | 0.230918551 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_littoralis_Drosophila_virilis | Drosophila | Drosophila_littoralis | Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Yes | sound | PI | 41.2000000 | 4.9600000 | 19.4000000 | 2.9000000 | 19.7000000 | 1.2500000 | 10 | 10 | 10 | 10 | 10 | 0.451619923 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_littoralis_Drosophila_virilis | Drosophila | Drosophila_littoralis | Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Yes | sound | IPI | 286.0000000 | 51.5000000 | 39.4000000 | 2.3200000 | 19.7000000 | 1.2500000 | 10 | 10 | 10 | 10 | 10 | 3.435973249 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_virilis | Drosophila | Drosophila_flavomontana | Drosophila_virilis | Drosophila_flavomontana_Drosophila_virilis | Yes | sound | PI | 27.1000000 | 2.3800000 | 22.9000000 | 2.1800000 | 17.5000000 | 2.0700000 | 19.7000000 | 1.2500000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | -0.193605661 | Viable | ||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_virilis | Drosophila | Drosophila_flavomontana | Drosophila_virilis | Drosophila_flavomontana_Drosophila_virilis | Yes | sound | IPI | 113.0000000 | 8.9300000 | 42.6000000 | 5.2100000 | 29.5000000 | 2.2200000 | 19.7000000 | 1.2500000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 2.005673474 | Viable | ||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_montana | Drosophila | Drosophila_flavomontana | Drosophila_montana | Drosophila_flavomontana_Drosophila_montana | Yes | sound | pause | 85.7000000 | 9.9400000 | 39.0000000 | 8.8800000 | 15.0000000 | 2.2100000 | 10 | 10 | 10 | 8 | 10 | 1.999563908 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_montana | Drosophila | Drosophila_flavomontana | Drosophila_montana | Drosophila_flavomontana_Drosophila_montana | Yes | sound | PI | 27.1000000 | 2.3800000 | 16.6000000 | 0.9200000 | 20.7000000 | 2.7100000 | 10 | 10 | 10 | 8 | 10 | -0.269872170 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_montana | Drosophila | Drosophila_flavomontana | Drosophila_montana | Drosophila_flavomontana_Drosophila_montana | Yes | sound | IPI | 113.0000000 | 8.9300000 | 55.6000000 | 9.0200000 | 35.7000000 | 2.1100000 | 10 | 10 | 10 | 8 | 10 | 1.089934291 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_littoralis | Drosophila | Drosophila_flavomontana | Drosophila_littoralis | Drosophila_flavomontana_Drosophila_littoralis | Yes | sound | pause | 85.7000000 | 9.9400000 | 128.0000000 | 19.1000000 | 245.0000000 | 52.4000000 | 10 | 10 | 10 | 5 | 10 | 0.933086029 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_littoralis | Drosophila | Drosophila_flavomontana | Drosophila_littoralis | Drosophila_flavomontana_Drosophila_littoralis | Yes | sound | PI | 27.1000000 | 2.3800000 | 20.8000000 | 0.8400000 | 41.2000000 | 4.9600000 | 10 | 10 | 10 | 5 | 10 | -0.039595049 | Inviable | |||||||||||||||||
| s20 | Paallysaho et al_2003 | table1 | Drosophila_flavomontana_Drosophila_littoralis | Drosophila | Drosophila_flavomontana | Drosophila_littoralis | Drosophila_flavomontana_Drosophila_littoralis | Yes | sound | IPI | 113.0000000 | 8.9300000 | 148.0000000 | 19.1000000 | 286.0000000 | 51.5000000 | 10 | 10 | 10 | 5 | 10 | 0.745479142 | Inviable | |||||||||||||||||
| s22 | Shaw_1996 | table4 | KW L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse period | 0.2710000 | 0.0100000 | 0.4640000 | 0.0140000 | 0.5960000 | 0.0150000 | 1.4230000 | 0.1500000 | 40 | 40 | 5 | 5 | 2 | 2 | 28 | 28 | 40 | 5 | 2 | 28 | 1.869566995 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | KW L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse rate | 3.6940000 | 0.1320000 | 2.1590000 | 0.0670000 | 1.6780000 | 0.0420000 | 0.7180000 | 0.0790000 | 40 | 40 | 5 | 5 | 2 | 2 | 28 | 28 | 40 | 5 | 2 | 28 | 1.838132903 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | KW L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Carrier frequency | 4.9270000 | 0.2460000 | 5.2930000 | 0.3040000 | 5.2930000 | 0.3040000 | 5.1180000 | 0.1990000 | 38 | 38 | 5 | 5 | 2 | 2 | 27 | 27 | 38 | 5 | 2 | 27 | -0.626238889 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | KW L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse duration | 39.7700000 | 4.4100000 | 38.4000000 | 2.3300000 | 38.7600000 | 0.3400000 | 38.3700000 | 4.5300000 | 37 | 37 | 5 | 5 | 2 | 2 | 27 | 27 | 37 | 5 | 2 | 27 | -0.629622089 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | OF L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse period | 0.2710000 | 0.0100000 | 0.5200000 | 0.0230000 | 0.5730000 | 0.0270000 | 1.0790000 | 0.0870000 | 40 | 40 | 10 | 10 | 4 | 4 | 27 | 27 | 40 | 10 | 4 | 27 | 1.443324350 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | OF L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse rate | 3.6940000 | 0.1320000 | 1.9250000 | 0.0860000 | 1.7480000 | 0.0800000 | 0.9330000 | 0.0780000 | 40 | 40 | 10 | 10 | 4 | 4 | 27 | 27 | 40 | 10 | 4 | 27 | 1.434681617 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | OF L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Carrier frequency | 4.9270000 | 0.2460000 | 4.9940000 | 0.0960000 | 5.0190000 | 0.2450000 | 5.0810000 | 0.2330000 | 38 | 38 | 10 | 10 | 4 | 4 | 25 | 25 | 38 | 10 | 4 | 25 | -0.637414533 | Viable | ||||||||
| s22 | Shaw_1996 | table4 | OF L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse duration | 39.7700000 | 4.4100000 | 39.9600000 | 1.8500000 | 35.9400000 | 3.3600000 | 38.1700000 | 0.6000000 | 37 | 37 | 10 | 10 | 4 | 4 | 25 | 25 | 37 | 10 | 4 | 25 | -0.621572586 | Viable | ||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_lummei_Drosophila_virilis | Drosophila | Drosophila_lummei | Drosophila_virilis | Drosophila_lummei_Drosophila_virilis | Yes | sound | inhbt_PL | 9.0000000 | 3.5774006 | 10.0000000 | 3.1841727 | 11.0000000 | 8.3708755 | 11.0000000 | 3.7425055 | 5 | 5 | 4 | 5 | 105 | 104 | 87 | 112 | -0.375733818 | Viable | ||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_lummei_Drosophila_virilis | Drosophila | Drosophila_lummei | Drosophila_virilis | Drosophila_lummei_Drosophila_virilis | Yes | sound | inhbt_CN | 3.0000000 | 0.9937224 | 3.0000000 | 0.9950540 | 3.0000000 | 3.2666831 | 4.0000000 | 0.9848699 | 5 | 5 | 4 | 5 | 105 | 104 | 87 | 112 | -0.241712907 | Viable | ||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_lummei_Drosophila_virilis | Drosophila | Drosophila_lummei | Drosophila_virilis | Drosophila_lummei_Drosophila_virilis | Yes | sound | inhbt_IPI | 51.0000000 | 16.0983025 | 53.0000000 | 26.8664575 | 48.0000000 | 30.2168190 | 46.0000000 | 30.5309658 | 5 | 5 | 4 | 5 | 105 | 104 | 87 | 112 | -0.525889171 | Viable | ||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_borealis_Drosophila_virilis | Drosophila | Drosophila_borealis | Drosophila_virilis | Drosophila_borealis_Drosophila_virilis | Yes | sound | inhbt_PL | 8.0000000 | 9.6613487 | 12.0000000 | 5.9309256 | 12.0000000 | 8.4409717 | 11.0000000 | 3.7425055 | 4 | 4 | 2 | 5 | 96 | 86 | 51 | 112 | -0.194316281 | Viable | ||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_borealis_Drosophila_virilis | Drosophila | Drosophila_borealis | Drosophila_virilis | Drosophila_borealis_Drosophila_virilis | Yes | sound | inhbt_CN | 2.0000000 | 1.8115029 | 3.0000000 | 1.8406321 | 3.0000000 | 1.5549158 | 4.0000000 | 0.9848699 | 4 | 4 | 2 | 5 | 96 | 86 | 51 | 112 | 0.382812357 | Viable | ||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_borealis_Drosophila_virilis | Drosophila | Drosophila_borealis | Drosophila_virilis | Drosophila_borealis_Drosophila_virilis | Yes | sound | inhbt_IPI | 28.0000000 | 57.9680922 | 34.0000000 | 61.5589175 | 30.0000000 | 60.4195866 | 46.0000000 | 30.5309658 | 4 | 4 | 2 | 5 | 96 | 86 | 51 | 112 | 0.079825624 | Viable | ||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_littoralis_Drosophila_virilis | Drosophila | Drosophila_littoralis | Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Yes | sound | inhbt_PL | 9.0000000 | 5.0099491 | 11.0000000 | 4.9521372 | 11.0000000 | 3.7425055 | 5 | 4 | 5 | 99 | 81 | 112 | -0.375733818 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_littoralis_Drosophila_virilis | Drosophila | Drosophila_littoralis | Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Yes | sound | inhbt_CN | 2.0000000 | 1.0019898 | 3.0000000 | 1.2380343 | 4.0000000 | 0.9848699 | 5 | 4 | 5 | 99 | 81 | 112 | 0.382812357 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_littoralis_Drosophila_virilis | Drosophila | Drosophila_littoralis | Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Yes | sound | inhbt_IPI | 62.0000000 | 40.6807864 | 34.0000000 | 35.4903165 | 46.0000000 | 30.5309658 | 5 | 4 | 5 | 99 | 81 | 112 | -0.225061190 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_montana_Drosophila_virilis | Drosophila | Drosophila_montana | Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Yes | sound | inhbt_PL | 12.0000000 | 41.0676782 | 13.0000000 | 8.4855081 | 11.0000000 | 3.7425055 | 5 | 5 | 5 | 114 | 131 | 112 | -0.550799722 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_montana_Drosophila_virilis | Drosophila | Drosophila_montana | Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Yes | sound | inhbt_CN | 3.0000000 | 4.9124017 | 4.0000000 | 1.3499672 | 4.0000000 | 0.9848699 | 5 | 5 | 5 | 114 | 131 | 112 | -0.241712907 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_montana_Drosophila_virilis | Drosophila | Drosophila_montana | Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Yes | sound | inhbt_IPI | 21.0000000 | 21.8110635 | 35.0000000 | 20.8280654 | 46.0000000 | 30.5309658 | 5 | 5 | 5 | 114 | 131 | 112 | 0.522933350 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_kanekoi_Drosophila_virilis | Drosophila | Drosophila_kanekoi | Drosophila_virilis | Drosophila_kanekoi_Drosophila_virilis | Yes | sound | inhbt_PL | 25.0000000 | 17.1493686 | 11.0000000 | 3.5013480 | 11.0000000 | 3.7425055 | 4 | 3 | 5 | 28 | 82 | 112 | 0.579710113 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_kanekoi_Drosophila_virilis | Drosophila | Drosophila_kanekoi | Drosophila_virilis | Drosophila_kanekoi_Drosophila_virilis | Yes | sound | inhbt_CN | 5.0000000 | 3.7281236 | 4.0000000 | 1.0298082 | 4.0000000 | 0.9848699 | 4 | 3 | 5 | 28 | 82 | 112 | -0.341119579 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_kanekoi_Drosophila_virilis | Drosophila | Drosophila_kanekoi | Drosophila_virilis | Drosophila_kanekoi_Drosophila_virilis | Yes | sound | inhbt_IPI | 218.0000000 | 92.2089242 | 36.0000000 | 23.6855892 | 46.0000000 | 30.5309658 | 4 | 3 | 5 | 28 | 82 | 112 | 1.711612257 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_flavomontana_Drosophila_lacicola | Drosophila | Drosophila_flavomontana | Drosophila_lacicola | Drosophila_flavomontana_Drosophila_lacicola | Yes | sound | inhbt_PL | 13.0000000 | 6.8200006 | 12.0000000 | 3.9527756 | 11.0000000 | 4.6213692 | 5 | 5 | 5 | 187 | 158 | 72 | -0.427512435 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_flavomontana_Drosophila_lacicola | Drosophila | Drosophila_flavomontana | Drosophila_lacicola | Drosophila_flavomontana_Drosophila_lacicola | Yes | sound | inhbt_CN | 3.0000000 | 1.1059460 | 3.0000000 | 0.9411370 | 3.0000000 | 1.0503112 | 5 | 5 | 5 | 187 | 158 | 72 | -0.684820633 | Inviable | ||||||||||||||||
| s25 | Suvanto et al_1994 | table 1&2 | Drosophila_flavomontana_Drosophila_lacicola | Drosophila | Drosophila_flavomontana | Drosophila_lacicola | Drosophila_flavomontana_Drosophila_lacicola | Yes | sound | inhbt_IPI | 31.0000000 | 15.8518933 | 35.0000000 | 15.0581927 | 62.0000000 | 38.0212646 | 5 | 5 | 5 | 187 | 158 | 72 | 0.382812357 | Inviable | ||||||||||||||||
| s26 | Tomaru&Oguma_1994 | table1 | A541.auraria_B16.biauraria | Drosophila | Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | Yes | sound | IPI | 21.4000000 | 1.9000000 | 15.7000000 | 0.5200000 | 16.0000000 | 0.5000000 | 12.9000000 | 0.9100000 | 6 | 6 | 10~20 | 10~20 | 10~20 | 10~20 | 10 | 10 | 6 | 22 | 15 | 10 | 180 | 220 | 150 | 300 | 0.094807394 | Viable | ||||
| s26 | Tomaru&Oguma_1994 | table1 | A541.auraria_B18.biauraria | Drosophila | Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | Yes | sound | IPI | 21.4000000 | 1.9000000 | 15.2000000 | 1.3100000 | 16.2000000 | 0.4200000 | 12.4000000 | 0.7600000 | 6 | 6 | 10~20 | 10~20 | 6 | 10~20 | 9 | 9 | 6 | 12 | 6 | 9 | 180 | 120 | 60 | 270 | 0.155695512 | Viable | ||||
| s26 | Tomaru&Oguma_1994 | table1 | A662.auraria_B18.biauraria | Drosophila | Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | Yes | sound | IPI | 18.9000000 | 1.9800000 | 16.6000000 | 0.3700000 | 15.8000000 | 0.4900000 | 12.4000000 | 0.7600000 | 8 | 8 | 5 | 5 | 6 | 6 | 9 | 9 | 8 | 5 | 6 | 9 | 240 | 50 | 60 | 270 | -0.035650543 | Viable | ||||
| s26 | Tomaru&Oguma_1994 | table1 | sn.cn.ba2.auraria_B16.biauraria | Drosophila | Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | Yes | sound | IPI | 19.9000000 | 0.7200000 | 16.3000000 | 0.6000000 | 12.9000000 | 0.9100000 | 10~20 | 10~20 | 10 | 10 | 11 | 27 | 10 | 110 | 270 | 300 | -0.017125774 | Inviable | ||||||||||||
| s26 | Tomaru&Oguma_1994 | table1 | A541.auraria_T544_triauraria | Drosophila | Drosophila_auraria | Drosophila_triauraria | Drosophila_auraria_Drosophila_triauraria | Yes | sound | IPI | 21.4000000 | 1.9000000 | 17.3000000 | 0.4300000 | 18.1000000 | 0.3900000 | 16.1000000 | 0.9600000 | 6 | 6 | 8 | 8 | 10 | 10 | 7 | 7 | 6 | 8 | 10 | 7 | 180 | 80 | 100 | 210 | -0.246503794 | Viable | ||||
| s26 | Tomaru&Oguma_1994 | table1 | A541.auraria_quadraria | Drosophila | Drosophila_auraria | Drosophila_quadraria | Drosophila_auraria_Drosophila_quadraria | Yes | sound | IPI | 21.4000000 | 1.9000000 | 17.1000000 | 0.4100000 | 17.6000000 | 0.2600000 | 15.6000000 | 0.6800000 | 6 | 6 | 9 | 9 | 6 | 6 | 5 | 5 | 6 | 9 | 6 | 5 | 180 | 90 | 60 | 150 | -0.197910842 | Viable | ||||
| s26 | Tomaru&Oguma_1994 | table1 | A541.auraria_subauraria | Drosophila | Drosophila_auraria | Drosophila_subauraria | Drosophila_auraria_Drosophila_subauraria | Yes | sound | IPI | 21.4000000 | 1.9000000 | 14.3000000 | 0.4000000 | 11.1000000 | 0.6400000 | 6 | 6 | 3 | 3 | 10 | 10 | 6 | 3 | 10 | 180 | 30 | 258 | 0.326282386 | Inviable | ||||||||||
| s26 | Tomaru&Oguma_1994 | table1 | Drosophila_triauraria_Drosophila_quadraria | Drosophila | Drosophila_triauraria | Drosophila_quadraria | Drosophila_triauraria_Drosophila_quadraria | Yes | sound | IPI | 16.1000000 | 0.9600000 | 14.9000000 | 0.3700000 | 16.5000000 | 0.5900000 | 15.6000000 | 0.6800000 | 7 | 7 | 9 | 9 | 6 | 6 | 5 | 5 | 210 | 9 | 6 | 150 | -0.636227681 | Viable | ||||||||
| s26 | Tomaru&Oguma_1994 | table1 | Drosophila_triauraria_Drosophila_biauraria | Drosophila | Drosophila_triauraria | Drosophila_biauraria | Drosophila_triauraria_Drosophila_biauraria | Yes | sound | IPI | 16.1000000 | 0.9600000 | 14.3000000 | 0.6000000 | 12.9000000 | 0.9100000 | 7 | 7 | 6 | 6 | 10 | 10 | 210 | 60 | 300 | -0.343509445 | Inviable | |||||||||||||
| s26 | Tomaru&Oguma_1994 | table1 | Drosophila_quadraria_Drosophila_biauraria | Drosophila | Drosophila_quadraria | Drosophila_biauraria | Drosophila_quadraria_Drosophila_biauraria | Yes | sound | IPI | 11.1000000 | 0.6400000 | 13.4000000 | 0.2700000 | 13.9000000 | 0.2800000 | 12.9000000 | 0.9100000 | 5 | 5 | 10 | 10 | 5 | 5 | 10 | 10 | 150 | 10 | 5 | 300 | -0.453345642 | Viable | ||||||||
| s26 | Tomaru&Oguma_1994 | table1 | Drosophila_quadraria_Drosophila_subauraria | Drosophila | Drosophila_quadraria | Drosophila_subauraria | Drosophila_quadraria_Drosophila_subauraria | Yes | sound | IPI | 11.1000000 | 0.6400000 | 13.3000000 | 0.5200000 | 11.1000000 | 0.6400000 | 5 | 5 | 10~20 | 10~20 | 10 | 10 | 150 | 140 | 258 | -0.684820633 | Inviable | |||||||||||||
| s26 | Tomaru&Oguma_1994 | table1 | Drosophila_biauraria_Drosophila_subauraria | Drosophila | Drosophila_biauraria | Drosophila_subauraria | Drosophila_biauraria_Drosophila_subauraria | Yes | sound | IPI | 12.9000000 | 0.9100000 | 11.8000000 | 0.3600000 | 11.5000000 | 0.5100000 | 11.1000000 | 0.6400000 | 10 | 10 | 8 | 8 | 8 | 8 | 10 | 10 | 300 | 8 | 8 | 258 | -0.453345642 | Viable | ||||||||
| s27 | Wells_1994 | table1 | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla | Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Yes | sound | Initial Volley frequency | 71.3000000 | 4.9193496 | 1.100 | 82.2000000 | 4.4090815 | 0.900 | 79.3000000 | 2.5000000 | 0.500 | 75.1000000 | 7.6733304 | 1.6000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 20 | 24 | 25 | 23 | -0.604843358 | Viable | ||||
| s27 | Wells_1994 | table1 | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla | Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Yes | sound | Middle Volley frequency | 58.2000000 | 2.2360680 | 0.500 | 67.9000000 | 2.9393877 | 0.600 | 64.3000000 | 3.5000000 | 0.700 | 59.1000000 | 3.3570821 | 0.7000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 20 | 24 | 25 | 23 | -0.661184331 | Viable | ||||
| s27 | Wells_1994 | table1 | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla | Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Yes | sound | End Volley frequency | 46.2000000 | 4.0249224 | 0.900 | 43.5000000 | 6.3686733 | 1.300 | 37.3000000 | 5.5000000 | 1.100 | 40.3000000 | 3.3570821 | 0.7000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 20 | 24 | 25 | 23 | -0.474376278 | Viable | ||||
| s27 | Wells_1994 | table1 | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla | Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Yes | sound | I-E Volley frequency | 25.1000000 | 7.6026311 | 1.700 | 38.7000000 | 7.3484692 | 1.500 | 41.9000000 | 4.5000000 | 0.900 | 34.7000000 | 8.6324967 | 1.8000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 20 | 24 | 25 | 23 | -0.185970935 | Viable | ||||
| s27 | Wells_1994 | table1 | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla | Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Yes | sound | Volley duration | 1586.0000000 | 652.9318494 | 146.000 | 892.0000000 | 274.3428512 | 56.000 | 718.0000000 | 170.0000000 | 34.000 | 604.0000000 | 105.5082935 | 22.0000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 20 | 24 | 25 | 23 | 0.802149039 | Viable | ||||
| s27 | Wells_1994 | table1 | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla | Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Yes | sound | Volley interval | 3499.0000000 | 1350.5850584 | 302.000 | 2220.0000000 | 357.6255024 | 73.000 | 1938.0000000 | 335.0000000 | 67.000 | 1199.0000000 | 153.4666087 | 32.0000 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 20 | 24 | 25 | 23 | 0.964790849 | Viable | ||||
| s28 | Shaw_2000 | table1 | KW L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse duration | 3.7500000 | 0.1400000 | 2.2200000 | 0.0700000 | 1.8800000 | 0.1200000 | 0.9400000 | 0.0500000 | 8 | 8 | 4 | 4 | 10 | 4 | 2 | 10 | 1.446343429 | Viable | ||||||||||||
| s28 | Shaw_2000 | table1 | SB L. paranigra | Laupala | Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | Yes | sound | Pulse duration | 3.7500000 | 0.1400000 | 2.4300000 | 0.0700000 | 2.0900000 | 0.1200000 | 1.0500000 | 0.0700000 | 8 | 8 | 4 | 4 | 10 | 2 | 2 | 10 | 1.275888706 | Viable | ||||||||||||
| s29 | Sasabe_et.al_2007 | fig2+text | Carabus_iwawakianus_Carabus_maiyasanus | Carabus | Carabus_iwawakianus | Carabus_maiyasanus | Carabus_iwawakianus_Carabus_maiyasanus | Yes | morphology | CPL | 1.2740000 | 0.0650000 | 1.7780000 | 0.1470000 | 1.8940000 | 0.1120000 | 2.5360000 | 0.1410000 | 13 | 13 | 33 | 33 | 33 | 26 | 96 | 22 | 0.375541208 | Viable | ||||||||||||
| s29 | Sasabe_et.al_2007 | fig2+text | Carabus_iwawakianus_Carabus_maiyasanus | Carabus | Carabus_iwawakianus | Carabus_maiyasanus | Carabus_iwawakianus_Carabus_maiyasanus | Yes | morphology | CPW | 0.7530000 | 0.0360000 | 0.4820000 | 0.0400000 | 0.4300000 | 0.0450000 | 0.3600000 | 0.0200000 | 13 | 13 | 33 | 33 | 33 | 26 | 96 | 22 | 0.451837988 | Viable |
Study.ID: ID of studies
Study.name: Including author name(s) and published year
Source: Source of phenotypc data in the primary studes
Cross.ID: Parental species used in the crossing expreiments. Distinguishes intraspecific populations.
Gennus: Studied genus
sp1.name and sp2.name: Crossed species. Ordered alphabetically.
species.pair: Parental species used in the crossing expreiments. Not distinguiscr intraspecific populations.
homologous: Measured traits are homologous across all crosses. If not, data has been excluded
trait.type: Sound or Morphology. Used in regressions bellow as a moderator.
trait: Trait name described in primary studies
Mn.~: Phenotypic mean of each cross
SD.~: SD of phenotype in each cross
SE.~: SE of phenotype in each cross
Parents.~: Number of parents used in each crossing. Usually not provided by the primary studies.
N.~: Number of individuals whose phenotype was measured
reps.~: Number of measurements, if replicated (especially in behavioral traits). Not used in the analyses bellow.
Pheno.divergence: Phenotypic divergence between parents in the focal trait, which is calculated as absolute value of log response ratio (lnRR) of phenotypic means. Used in regressions bellow as a moderator.
The species-level moderator dataset of this study.
meta <- read.xlsx(
"../data/original.data.xlsx", sheet = "Species.level.moderators"
# Same data is also saved as "../data/original.data.Species.level.moderators.txt"
) %>%
mutate_at(vars(contains("divergence")), as.numeric) %>%
# Logalize genetic divergence
mutate_at(vars(contains("divergence")), log10)
# making a scrollable table
kable(meta, "html") %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| sp1.name | sp2.name | species.pair | Cross.ID | Distribution | Source_distribution | taxa | souce_sex.determinaton | sex.determination | Hetero.sex | Genet.divergence | Survival.sp1 | Survival.hyb12 | Survival.hyb21 | Survival.sp2 | surv.relat.hyb12 | surv.relat.hyb21 | surv.File | surv.Source | surv.Study.name | comments | parentals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | A541.auraria_B16.biauraria | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture) | ||||||||
| Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | A541.auraria_B18.biauraria | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture) | ||||||||
| Drosophila_auraria | Drosophila_quadraria | Drosophila_auraria_Drosophila_quadraria | A541.auraria_quadraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -3.0000000 | NA | NA | |||||||||
| Drosophila_auraria | Drosophila_subauraria | Drosophila_auraria_Drosophila_subauraria | A541.auraria_subauraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | |||||||||
| Drosophila_auraria | Drosophila_triauraria | Drosophila_auraria_Drosophila_triauraria | A541.auraria_T544_triauraria | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -2.6989700 | NA | NA | |||||||||
| Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | A662.auraria_B18.biauraria | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture) | ||||||||
| Agrotis_ipsilon | Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Agrotis_ipsilon_Agrotis_segetum | Overlap | original study | Lepidoptera | Traut W. K. Sahara and F. Marec. “Sex chromosomes and sex determination in Lepidoptera.” Sexual Development 1.6 (2007): 332-346. | ZW/Z0 | Female | -1.3098039 | 65 | 1 | 0 | 60 | -4.1351665567423561 | NA | Gadenne_PheromoneReceptor_JChemEcol.1997 | table1 | Gadenne et al_1997 | ||
| Drosophila_melanogaster | Drosophila_simulans | Drosophila_melanogaster_Drosophila_simulans | Basc | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.2924298 | 1 | 0.5 | 0.2 | 1 | -0.69314718055994529 | -1.6094379124341003 | reviewed in Sawamura_ | ||||
| Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | BGxMutant | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.7212464 | NA | NA | |||||||||
| Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | BGxOxnard | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.7212464 | NA | NA | |||||||||
| Drosophila_mauritiana | Drosophila_simulans | Drosophila_mauritiana_Drosophila_simulans | BGxSPD(wildtype) | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.7212464 | NA | NA | |||||||||
| Carabus_iwawakianus | Carabus_maiyasanus | Carabus_iwawakianus_Carabus_maiyasanus | Carabus_iwawakianus_Carabus_maiyasanus | Overlap | original study | Coleoptera | Serrano J. and J. S. Yadav. “Chromosome numbers and sex-determining mechanisms in adephagan Coleoptera.” The Coleopterists’ Bulletin (1984): 335-357. | XY | Male | -1.8538720 | |||||||||||
| Drosophila_madeirensis | Drosophila_subobscura | Drosophila_madeirensis_Drosophila_subobscura | chcu | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.5435714 | NA | NA | |||||||||
| Chorthippus_biguttulus | Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Chorthippus_biguttulus_Chorthippus_brunneus | Overlap | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0/XY | Male | -0.0710923 | 0.59322033898305082 | 0.5 | 0.72368421052631582 | 0.35294117647058826 | 5.5341989352007639E-2 | 0.42508901485809281 | Perdeck_1958_Grasshopper | table7 | wild but caught in juvenile stage | ||
| Chrysoperla_johnsoni | Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Chrysoperla_johnsoni_Chrysoperla_plorabunda | Overlap | original study | Neuroptera | Tauber Catherine A. and Maurice J. Tauber. “Inheritance of seasonal cycles in Chrysoperla (Insecta: Neuroptera).” Genetics Research 49.3 (1987): 215-223. | XY | Male | -1.9586073 | 0.62551999999999996 | 0.45435714285714285 | 0.489375 | 0.54733333333333334 | -0.25516407769093707 | -0.1809185580094152 | Wells_1993_Lacewings | table1 | |||
| Drosophila_biauraria | Drosophila_subauraria | Drosophila_biauraria_Drosophila_subauraria | Drosophila_biauraria_Drosophila_subauraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.9586073 | NA | NA | |||||||||
| Drosophila_borealis | Drosophila_virilis | Drosophila_borealis_Drosophila_virilis | Drosophila_borealis_Drosophila_virilis | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -0.9208188 | 0.99 | NA | NA | Parents: ?? (mass culture)_SD estimated by Wan et al 2014 | |||||||
| Drosophila_buzzatii | Drosophila_koepferae | Drosophila_buzzatii_Drosophila_koepferae | Drosophila_buzzatii_Drosophila_koepferae | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.0224284 | NA | NA | |||||||||
| Drosophila_flavomontana | Drosophila_lacicola | Drosophila_flavomontana_Drosophila_lacicola | Drosophila_flavomontana_Drosophila_lacicola | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.0047512 | NA | NA | Parents: ?? (mass culture)_SD estimated by Wan et al 2014 | ||||||||
| Drosophila_flavomontana | Drosophila_littoralis | Drosophila_flavomontana_Drosophila_littoralis | Drosophila_flavomontana_Drosophila_littoralis | Allopatry | original paper | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -0.1938200 | NA | NA | |||||||||
| Drosophila_flavomontana | Drosophila_montana | Drosophila_flavomontana_Drosophila_montana | Drosophila_flavomontana_Drosophila_montana | Allopatry | original paper | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.1048284 | NA | NA | |||||||||
| Drosophila_flavomontana | Drosophila_virilis | Drosophila_flavomontana_Drosophila_virilis | Drosophila_flavomontana_Drosophila_virilis | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.0132587 | 0.99 | NA | NA | ||||||||
| Drosophila_heteroneura | Drosophila_silvestris | Drosophila_heteroneura_Drosophila_silvestris | Drosophila_heteroneura_Drosophila_silvestris | Overlap | “Drosophila heteroneura and Drosophila silvestris: Head Shapes, Behavior and Evolution” | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -2.0457575 | NA | NA | |||||||||
| Drosophila_kanekoi | Drosophila_virilis | Drosophila_kanekoi_Drosophila_virilis | Drosophila_kanekoi_Drosophila_virilis | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -0.9507820 | 0.99 | NA | NA | Parents: ?? (mass culture)_SD estimated by Wan et al 2014 | |||||||
| Drosophila_littoralis | Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Drosophila_littoralis_Drosophila_virilis | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -0.9136402 | 0.95499999999999996 | 0 | 0.16 | 0.99 | 0 | -1.8046962602587744 | Orr_Coyne_1989_Drosophila | fertility_table2&3 | |||
| Drosophila_lummei | Drosophila_virilis | Drosophila_lummei_Drosophila_virilis | Drosophila_lummei_Drosophila_virilis | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.2441251 | 0.97499999999999998 | 0.98 | 0.95 | 0.99 | -2.5477720787986644E-3 | -3.3638359148829802E-2 | Orr_Coyne_1989_Drosophila | fertility_table2&3 | Parents: ?? (mass culture)_SD estimated by Wan et al 2014 | ||
| Drosophila_montana | Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Drosophila_montana_Drosophila_virilis | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.0457575 | 0.99 | NA | NA | ||||||||
| Drosophila_persimilis | Drosophila_pseudoobscura | Drosophila_persimilis_Drosophila_pseudoobscura | Drosophila_persimilis_Drosophila_pseudoobscura | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -2.2218487 | NA | NA | |||||||||
| Drosophila_quadraria | Drosophila_biauraria | Drosophila_quadraria_Drosophila_biauraria | Drosophila_quadraria_Drosophila_biauraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -2.3979400 | NA | NA | |||||||||
| Drosophila_quadraria | Drosophila_subauraria | Drosophila_quadraria_Drosophila_subauraria | Drosophila_quadraria_Drosophila_subauraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | |||||||||
| Drosophila_triauraria | Drosophila_biauraria | Drosophila_triauraria_Drosophila_biauraria | Drosophila_triauraria_Drosophila_biauraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | |||||||||
| Drosophila_triauraria | Drosophila_quadraria | Drosophila_triauraria_Drosophila_quadraria | Drosophila_triauraria_Drosophila_quadraria | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -3.0000000 | NA | NA | |||||||||
| Coturnix_coturnix_coturnix | Coturnix_japonica | Coturnix_coturnix_coturnix_Coturnix_japonica | Coturnix_coturnix_coturnix_Coturnix_japonica | Allopatry | original study | Aves | Ezaz Tariq et al. “Relationships between vertebrate ZW and XY sex chromosome systems.” Current Biology 16.17 (2006): R736-R743. | ZW | Female | -1.7447275 | 0.347634 | 0.38592000000000004 | 0.32175000000000004 | 0.10447989191213911 | -7.7375355710390822E-2 | Deregnaucourt_2002_Quail | table2&3 | ||||
| Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | f^2 | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.3872161 | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA | NA | |||||
| Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | FC | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.3872161 | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA | NA | |||||
| Gryllus_armatus | Gryllus_rubens | Gryllus_armatus_Gryllus_rubens | Gryllus_armatus_Gryllus_rubens | Allopatry | original paper | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | NA | NA | N of indv_N of songs. N of parental pairs: 12 | |||||||||
| Gryllus_campestris | Gryllus_rubens | Gryllus_campestris_Gryllus_rubens | Gryllus_campestris_Gryllus_rubens | Allopatry | original paper | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | 0.6626634 | NA | NA | N of indv_N of songs. N of parental pairs: 12 | ||||||||
| Gryllus_rubens | Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Gryllus_rubens_Gryllus_texensis | Overlap | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | -2.0457575 | 76.5 | 69.099999999999994 | 52.4 | 43.8 | 0.13871328835293623 | -0.13793485109369141 | Cade_1989_Gryllus.mortality | table1 | integerはtexensis | ||
| Drosophila_madeirensis | Drosophila_subobscura | Drosophila_madeirensis_Drosophila_subobscura | H27 | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.5435714 | NA | NA | |||||||||
| Haplochromis_burtoni | Haplochromis_nubilus | Haplochromis_burtoni_Haplochromis_nubilus | Haplochromis_burtoni_Haplochromis_nubilus | Allopatry | original study | Cichliformes | 0 | NA | NA | ||||||||||||
| Hyla_chrysoscelis | Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Hyla_chrysoscelis_Hyla_femoralis | Overlap | original study | Anura | Miura I. “The late replication banding patterns of chromosomes are highly conserved in the genera Rana Hyla and Bufo (Amphibia: Anura).” Chromosoma 103.8 (1995): 567-574. | XY | Male | -0.0070049 | NA | NA | |||||||||
| Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | KW L. paranigra | Allopatry | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | NA | NA | ||||||||||
| Drosophila_madeirensis | Drosophila_subobscura | Drosophila_madeirensis_Drosophila_subobscura | mad | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.5435714 | NA | NA | |||||||||
| Drosophila_madeirensis | Drosophila_subobscura | Drosophila_madeirensis_Drosophila_subobscura | Mdh86 | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | 0.5435714 | NA | NA | |||||||||
| Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | musculus.1 | Allopatry | original study | Rodentia | Ezaz Tariq et al. “Relationships between vertebrate ZW and XY sex chromosome systems.” Current Biology 16.17 (2006): R736-R743. | XY | Male | -1.5376020 | NA | NA | N of indv_genetic distance_first 20 sequences were used_respectively | ||||||||
| Mus_musculus_domesticus | Mus_musculus_musculus | Mus_musculus_domesticus_Mus_musculus_musculus | musculus.3 | Allopatry | original study | Rodentia | Ezaz Tariq et al. “Relationships between vertebrate ZW and XY sex chromosome systems.” Current Biology 16.17 (2006): R736-R743. | XY | Male | -1.5376020 | NA | NA | N of indv_genetic distance_first 20 sequences were used_respectively | ||||||||
| Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | OF L. paranigra | Allopatry | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | NA | NA | ||||||||||
| Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | Oxnard | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.3872161 | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA | NA | |||||
| Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | parallelus_localityA | Overlap | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | NA | NA | ||||||||||
| Chorthippus_parallelus_erythropus | Chorthippus_parallelus_parallelus | Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus | parallelus_localityB | Overlap | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | NA | NA | ||||||||||
| Drosophila_mauritiana | Drosophila_sechellia | Drosophila_mauritiana_Drosophila_sechellia | pn_j_irX85 | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | 0 | NA | NA | ||||||||
| Pundamilia_nyererei | Pundamilia_pundamilia | Pundamilia_nyererei_Pundamilia_pundamilia | Pundamilia_nyererei_Pundamilia_pundamilia | Overlap | original study | Cichliformes | 4.8600000000000003 | 4.84 | 4.51 | 4.57 | 2.616580464311944E-2 | -4.445176257083381E-2 | Van Der Sluijs_2008_Cichlid | text | |||||||
| Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | RM | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.3872161 | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA | NA | |||||
| Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | RMdet | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.3872161 | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA | NA | |||||
| Laupala_kohalensis | Laupala_paranigra | Laupala_kohalensis_Laupala_paranigra | SB L. paranigra | Allopatry | original study | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0/XY | Male | NA | NA | ||||||||||
| Drosophila_auraria | Drosophila_biauraria | Drosophila_auraria_Drosophila_biauraria | sn.cn.ba2.auraria_B16.biauraria | Overlap | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.4814861 | NA | NA | parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture) | ||||||||
| Spodoptera_descoinsi | Spodoptera_latifascia | Spodoptera_descoinsi_Spodoptera_latifascia | Spodoptera_descoinsi_Spodoptera_latifascia | Overlap | original paper | Lepidoptera | Traut W. K. Sahara and F. Marec. “Sex chromosomes and sex determination in Lepidoptera.” Sexual Development 1.6 (2007): 332-346. | ZW/Z0 | Female | -1.4685211 | NA | NA | |||||||||
| Teleogryllus_commodus | Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Teleogryllus_commodus_Teleogryllus_oceanicus | Overlap | The Australian crickets (Orthoptera: Gryllidae) | Orthoptera | Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231. | X0 | Male | 0.1182647 | NA | NA | N of indv_N of songs. N of parental pairs: 12 | ||||||||
| Drosophila_melanogaster | Drosophila_simulans | Drosophila_melanogaster_Drosophila_simulans | v | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.2924298 | 1 | 0.5 | 0.2 | 1 | -0.69314718055994529 | -1.6094379124341003 | reviewed in Sawamura_ | ||||
| Drosophila_melanogaster | Drosophila_simulans | Drosophila_melanogaster_Drosophila_simulans | XsimYmel | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.2924298 | 1 | 0.5 | 0.2 | 1 | -0.69314718055994529 | -1.6094379124341003 | reviewed in Sawamura_ | ||||
| Drosophila_sechellia | Drosophila_simulans | Drosophila_sechellia_Drosophila_simulans | XXY | Allopatry | original study | Diptera | Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078. | XY | Male | -1.3872161 | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA_possibly_high | NA | NA |
#++++++++++++++++++++++++++++++++++++++++++++++
# Combine phenotypic data and moderators data
#++++++++++++++++++++++++++++++++++++++++++++++
original.dat <- left_join(pheno, meta)
# add unique ID for each effect size (row)
original.dat$ES.ID <- paste("ES",sprintf("%03d",c(1:dim(original.dat)[1])), sep="")
# #+++++++++++++++++++++++++++++++++
# print("Scaling")
# #+++++++++++++++++++++++++++++++++
# print("Phenotypic divergence ratio")
# attributes(pheno$Pheno.divergence)
# print("Minimum scaled value")
# min(pheno$Pheno.divergence)
# print("Maximum scaled value")
# max(pheno$Pheno.divergence)
Reciprocal: Viability of reciprocal hybrids, with two levels - Viable or Inviable
Genet.divergence: Natural logarithm of genetic distance at mtDNA COI region (>450bp).
Distribution: Distribution overlap - Allopatry or Overlap
Hetero.sex: Heterogametic sex - Female (ZW and Z0) or Male (XY and X0)
#+++++++++++++++++++++#
# Phylogeny
#+++++++++++++++++++++#
# species pairs data
taxa <- meta %>%
select(-Cross.ID) %>%
distinct_all(.keep_all = TRUE) %>%
mutate_at("taxa", as.factor)
# matching names from open tree taxonomy
order <- tnrs_match_names(
names = levels(taxa$taxa) %>%
str_replace_all("_", " "),
context_name = "Animals"
)
# which names return more than 1 match?
multimatch <- order$ott_id[order$number_matches != 1]
# inspect(order, ott_id = multimatch[1]) # Anura, confirmed adopted ott_id is correct
# inspect(order, ott_id = multimatch[2]) # Neuroptera, confirmed adopted ott_id is correct
# Create taxonomic tree
tree <- tol_induced_subtree(ott_ids = order$ott_id)
# Remove ott ids from tip label
tree$tip.label %<>%
strip_ott_ids(remove_underscores=TRUE)
tree$tip.label[8] <- "Anura"
# # N of species pairs at each order
# factor(meta.sum$taxa, levels = as.vector(tree$tip.label))
plot.phylo <- ggtree(tree) +
geom_tiplab(
geom = "image",
image = paste("../data/pic", tree$tip.label, "png", sep = "."),
size = 0.1, offset = 3, hjust = 1
) +
geom_tiplab(
geom = "text",
label = tree$tip.label,
align = 1, size = 3
) +
xlim(NA, 7)
# images from phylopic #
# Lepidoptera (genus Catocala) "c224abfd-ee39-4923-98e5-c2606dcc56cb",
# Diptera (Drosophila) "0cd6cc9f-683c-470e-a4a6-3b68beb826fa"
# Orthoptera (cricket) "b80d830b-155a-4ca5-9119-9a9fde019cc6"
# Rodentia (mouse), drawn by David Liao "0f6af3d8-49d2-4d75-8edf-08598387afde"
# Aves (Quail) "42f85a2b-7517-439a-8dc3-b745a35c035d"
# Anura (genus Hyla) "A22D353D-B045-4044-9432-8F340B9A3104",
# Cichliformes (Nile tilapia), drawn by Milton Tan "84c7e672-2593-44a6-a807-cffbd3156cc5"
#+++++++++++++++++++++#
# Summary
#+++++++++++++++++++++#
summary <- original.dat %>%
group_by(taxa) %>%
summarise(
'Species pair' = length(unique(species.pair)),
'Obser- vation' = length(unique(ES.ID)),
Study = length(unique(Study.ID))
) %>%
# Order taxon so that match with phylogenetic tree topology
within(taxa <- ordered(taxa, levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")))
## Table ###
summary %>%
summarise_if(is.numeric, sum) %>%
mutate(taxa = "Total") %>%
bind_rows(summary, .) %>%
kable("html", digits = 3) %>%
kable_styling("striped", position = "left")
| taxa | Species pair | Obser- vation | Study |
|---|---|---|---|
| Anura | 1 | 5 | 1 |
| Aves | 1 | 1 | 1 |
| Cichliformes | 2 | 6 | 3 |
| Coleoptera | 1 | 2 | 1 |
| Diptera | 23 | 65 | 9 |
| Lepidoptera | 2 | 13 | 2 |
| Neuroptera | 1 | 6 | 1 |
| Orthoptera | 7 | 63 | 6 |
| Rodentia | 1 | 240 | 1 |
| Total | 39 | 401 | 25 |
## Plot ###
plot.sum <- ggplot(
# Make tidy summary data
summary %>%
gather(key = metrics, value = N, -c(taxa)) %>%
# Order metrics
within(
metrics <- ordered(
metrics,
levels = c("Study", "Species pair", "Obser- vation")
)
),
aes(x = "", y = N, fill = taxa)) +
geom_bar(width = 1, stat = "identity") +
scale_fill_brewer(palette = "Paired") +
facet_wrap(
scale = "free", "metrics",
# strip text into two lines
labeller = label_wrap_gen(width = 8)
) +
xlab("") +
stackedbartheme +
theme(
strip.text = element_text(size = 15),
axis.text.y = element_text(size = 14)
)
#+++++++++++++++++#
# Combine figures
#+++++++++++++++++#
# plot.all <- plot.phylo + plot.sum
plot.sum
# save(
# plot = plot.all,
# file = "../Analysis/data.svg", height = 5, width = 8.5
# )
Figure 1a
Summarised distributions of categorical species-level moderators in each taxon (order).
metavars <- c("Reciprocal", "Distribution", "Hetero.sex")
meta.unique <- original.dat %>%
distinct_at("species.pair", .keep_all = TRUE) %>%
mutate_at(metavars, as.factor)
meta.all.sp <- meta.unique %>%
group_by(taxa) %>%
summarise(Total = length(species.pair))
for (i in metavars) {
bind_rows(
# At each taxon
meta.unique %>%
group_by_("taxa", i) %>%
summarise(species = length(species.pair))
,
# All taxon
meta.unique %>%
group_by_(i) %>%
summarise(species = length(unique(species.pair))) %>%
mutate(taxa = "Total")
) %>%
ungroup %>%
spread(key = i, value = "species") %>%
mutate_all(~replace(., is.na(.), 0)) %>% # replace NA by 0
as.data.frame %>%
# All species pair at each taxon: summing columns
mutate(Total = rowSums(.[2:(1+nlevels(meta.unique[, i]))])) %>%
# Calc % of level 2 (i.e. male hetero, ...)
mutate('%' = round(.[3]/Total*100, 1)) %>%
rename(!!paste0(levels(meta.unique[, i])[2], "%") := "%") %>%
# Table output
kable("html", digits = 3, caption = i) %>%
kable_styling("striped", position = "left") %>%
print()
}
| taxa | Inviable | Viable | Total | Viable% |
|---|---|---|---|---|
| Anura | 0 | 1 | 1 | 100.0 |
| Aves | 0 | 1 | 1 | 100.0 |
| Cichliformes | 1 | 1 | 2 | 50.0 |
| Coleoptera | 0 | 1 | 1 | 100.0 |
| Diptera | 11 | 12 | 23 | 52.2 |
| Lepidoptera | 1 | 1 | 2 | 50.0 |
| Neuroptera | 0 | 1 | 1 | 100.0 |
| Orthoptera | 2 | 5 | 7 | 71.4 |
| Rodentia | 1 | 0 | 1 | 0.0 |
| Total | 16 | 23 | 39 | 59.0 |
| taxa | Allopatry | Overlap | Total | Overlap% |
|---|---|---|---|---|
| Anura | 0 | 1 | 1 | 100.0 |
| Aves | 1 | 0 | 1 | 0.0 |
| Cichliformes | 1 | 1 | 2 | 50.0 |
| Coleoptera | 0 | 1 | 1 | 100.0 |
| Diptera | 19 | 4 | 23 | 17.4 |
| Lepidoptera | 0 | 2 | 2 | 100.0 |
| Neuroptera | 0 | 1 | 1 | 100.0 |
| Orthoptera | 3 | 4 | 7 | 57.1 |
| Rodentia | 1 | 0 | 1 | 0.0 |
| Total | 25 | 14 | 39 | 35.9 |
| taxa | Female | Male | <NA> | Total | Male% |
|---|---|---|---|---|---|
| Anura | 0 | 1 | 0 | 1 | 100.0 |
| Aves | 1 | 0 | 0 | 1 | 0.0 |
| Cichliformes | 0 | 0 | 2 | 0 | |
| Coleoptera | 0 | 1 | 0 | 1 | 100.0 |
| Diptera | 0 | 23 | 0 | 23 | 100.0 |
| Lepidoptera | 2 | 0 | 0 | 2 | 0.0 |
| Neuroptera | 0 | 1 | 0 | 1 | 100.0 |
| Orthoptera | 0 | 7 | 0 | 7 | 100.0 |
| Rodentia | 0 | 1 | 0 | 1 | 100.0 |
| Total | 3 | 34 | 2 | 37 | 91.9 |
full_join(
# All species pair
original.dat %>%
select(c("taxa", "species.pair")) %>%
distinct_all() %>%
group_by(taxa) %>%
summarise('All species' = length(species.pair)),
# Species pair used in meta-regression
original.dat %>%
filter(!is.na(sex.determination), !is.na(Genet.divergence)) %>%
select(c("taxa", "species.pair")) %>%
distinct_all() %>%
group_by(taxa) %>%
summarise('Metadata available' = length(species.pair))
) %>%
mutate_all(~replace(., is.na(.), 0)) %>% # replace NA by 0
kable("html", caption = "Numbers of all species pairs, and species pairs used in meta-regression
") %>%
kable_styling("striped", position = "left")
| taxa | All species | Metadata available |
|---|---|---|
| Anura | 1 | 1 |
| Aves | 1 | 1 |
| Cichliformes | 2 | 0 |
| Coleoptera | 1 | 1 |
| Diptera | 23 | 23 |
| Lepidoptera | 2 | 2 |
| Neuroptera | 1 | 1 |
| Orthoptera | 7 | 4 |
| Rodentia | 1 | 1 |
Two fish and three orthoptera species-pairs were not used in meta-regression and Bayesian phylogenetic mixed models because of the lack of data for genetic distance or sex-determination system (fish, Haplochromis burtoni × H. nubilus and Pundamilia nyererei × P. pundamilia; orthoptera, Chorthippus parallelus erythropus × C. parallelus parallelus, Gryllus armatus × G. rubens and Laupala kohalensis × L. paranigra). We did not assign value of the heterogametic sex in cichlid fishes that undergo frequent evolutionary changes in sex-determination system Yoshida et al. 2011.
Currently, dataset is composed of
‘species 1’ and ‘species 2,’ ordered alphabetically, and ‘hybrid12’ (sp1 mother & sp2 father) and ‘hybrid 21’ (sp2 mother & sp1 father)
To investigate paternal effect in hybrids using difference in mean trait value between reciprocal cross, it’s better to allign species with larger trait value (spLLM) and smaller value (spSSM) in each traits. Name of hybrids will be changed according to change in names of parental species (hybridLSM & hybridSLM).
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 >= sp2 in phenotypic mean
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp1sp2.mean <- original.dat %>%
filter(Mn.sp1 >= Mn.sp2) # filtering according to mean trait value of parentals
# change column name from sp1 or sp2 -> spL or spS
names(sp1sp2.mean) <- gsub("1", "L", names(sp1sp2.mean))
names(sp1sp2.mean) <- gsub("2", "S", names(sp1sp2.mean))
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 < sp2 in phenotypic mean
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp2sp1.mean <- original.dat %>%
filter(Mn.sp1 < Mn.sp2)
# change column name from sp1 or sp2 -> spS or spL
names(sp2sp1.mean) <- gsub("1", "S", names(sp2sp1.mean))
names(sp2sp1.mean) <- gsub("2", "L", names(sp2sp1.mean))
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# combine dataset & sort by ES.ID
#+++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(sp1sp2.mean, sp2sp1.mean) %>%
arrange(ES.ID) %>%
write.csv("../data/dat.mean.csv", quote=F,row.names = F)
Data set created here, dat.mean.csv, is used in calculating effect sizes for analyses investigating phenotypic mean and novelty in phenotypic means.
To investigate paternal effect in hybrids using difference in coefficient of variation (CV) between reciprocal cross, it’s better to allign species with larger CV (spLLV) and smaller CV (spSSV) in each traits. Name of hybrids will be changed according to change in names of parental species (hybridLSV & hybridSLV).
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 >= sp2 in phenotypic variation (CV)
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp1sp2.var <- original.dat %>%
filter(SD.sp1/Mn.sp1 >= SD.sp2/Mn.sp2) # filtering according to CV of parentals
# change column name from sp1 or sp2 -> spL or spS
names(sp1sp2.var) <- gsub("1", "L", names(sp1sp2.var))
names(sp1sp2.var) <- gsub("2", "S", names(sp1sp2.var))
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 < sp2 in phenotypic variation (CV)
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp2sp1.var <- original.dat %>%
filter(SD.sp1/Mn.sp1 < SD.sp2/Mn.sp2)
# change column name from sp1 or sp2 -> spS or spL
names(sp2sp1.var) <- gsub("1", "S", names(sp2sp1.var))
names(sp2sp1.var) <- gsub("2", "L", names(sp2sp1.var))
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# combine dataset & sort by ES.ID
#+++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(sp1sp2.var, sp2sp1.var) %>%
arrange(ES.ID) %>%
write.csv("../data/dat.CV.csv", quote=F,row.names = F)
Data set created here, dat.CV.csv, is used in calculating effect sizes for analyses investigating phenotypic variation and novelty in phenotypic variabilities.
We categorised phenotypic novelty (novel or non-novel) and relative phenotypic variability (CV) compared to parents (smaller, intermediate, larger) at each trait.
bind_rows(
# Assess novel phenotype & variability expression in hyb12
original.dat %>%
drop_na(Mn.hyb12, SD.hyb12) %>%
mutate(
cross = "1x2",
# Relative trait size
relative.mean = ifelse(
# Exceeding upper range
Mn.hyb12 > Mn.sp1 & Mn.hyb12 > Mn.sp2,
"Larger",
ifelse(
# Exceeding lower range
Mn.hyb12 < Mn.sp1 & Mn.hyb12 < Mn.sp2,
"Smaller",
"Intermediate"
)
),
# Relative variability size
relative.var = ifelse(
# Exceeding upper range: 1
SD.hyb12/Mn.hyb12 > SD.sp1/Mn.sp1 &
SD.hyb12/Mn.hyb12 > SD.sp2/Mn.sp2,
"Larger",
ifelse(
# Exceeding lower range: "Smaller"
SD.hyb12/Mn.hyb12 < SD.sp1/Mn.sp1 &
SD.hyb12/Mn.hyb12 < SD.sp2/Mn.sp2,
"Smaller",
"Intermediate"
)
)
),
# Assess novel phenotype & variability expression in hyb12
original.dat %>%
drop_na(Mn.hyb21, SD.hyb21) %>%
mutate(
cross = "2x1",
# Relative trait size
relative.mean = ifelse(
# Exceeding upper range: 1
Mn.hyb21 > Mn.sp1 & Mn.hyb21 > Mn.sp2,
"Larger",
ifelse(
# Exceeding lower range: "Smaller"
Mn.hyb21 < Mn.sp1 & Mn.hyb21 < Mn.sp2,
"Smaller",
"Intermediate"
)
) ,
# Relative variability size
relative.var = ifelse(
# Exceeding upper range: 1
SD.hyb21/Mn.hyb21 > SD.sp1/Mn.sp1 &
SD.hyb21/Mn.hyb21 > SD.sp2/Mn.sp2,
"Larger",
ifelse(
# Exceeding lower range: "Smaller"
SD.hyb21/Mn.hyb21 < SD.sp1/Mn.sp1 &
SD.hyb21/Mn.hyb21 < SD.sp2/Mn.sp2,
"Smaller",
"Intermediate"
)
)
)
) %>%
mutate(mean.novelty = ifelse(relative.mean == "Intermediate", "Nonnovel", "Novel")) %>%
write.csv("../data/dat.novelty.csv", quote=F,row.names = F)
Data set created here, dat.novelty.csv, is used in investigating phenotypic variability of novel phenotype.
We created data subset according to taxon (all taxon or insect only) and novelty in phenotypic means (all traits or non-novel traits - trait size of hybrids does not exceed size range of parents). We conduct identical analyses for full data and data subsets, and checked robustness of our results.
To quantify phenotypic difference from spSSM to the other crosses, we calculated log response ratio (lnRR) from phenotypic mean of spSSM to those of the other crosses (hybLSM, hybSLM and spLLM).
Effecct size was calculated by using escalc function in metafor package
# load phenotypic data
dat <- read.csv("../data/dat.mean.csv", head = TRUE)
# Order by taxon
dat$taxa <- ordered(
dat$taxa,
levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
)
## Difference between hybrids and smaller species (spS) ###
mean.dif <- foreach(
cr = c("hybLS", "hybSL", "spL"),
.combine = `rbind`
) %do% {
## lnRR ###
escalc(
measure = "ROM",
# N of hybrids
n1i = dat[, paste("N", cr, sep = ".")],
# N of parentals
n2i = dat[, "N.spS"],
# Mean of hybrids
m1i = dat[, paste("Mn", cr, sep = ".")],
# Mean of parentals
m2i = dat[, "Mn.spS"],
# SD of hybrids
sd1i = dat[, paste("SD", cr, sep = ".")],
# SD of parentals
sd2i = dat[, "SD.spS"],
data = dat
) %>%
rename(lnRR.es = yi, lnRR.sv = vi) %>%
# indicate type of hybrid
mutate(cross = cr)
} %>%
drop_na(lnRR.sv) %>%
arrange(ES.ID) %>%
left_join(., dat)
# Dataset able to compare reciprocal crosses
write.csv(
mean.dif %>%
filter(Reciprocal == "Viable"),
"../data/mean.ES.general.csv", row.names = F
)
res <- rma(
yi = lnRR.es, vi = lnRR.sv,
data = mean.dif, method="FE"
)
## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-2, 4))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-2, 4))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-2, 4))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-2, 4))
Checked effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140) and confirmed that those data were correctly imported.
outliers <- mean.dif %>%
# vi (sampling variance)
mutate(SE = sqrt(lnRR.sv)) %>%
arrange(desc(SE)) %>%
filter(SE > 1.2 | 1/SE > 140) %>%
select(ES.ID, Study.name, SE, cross, Reciprocal, trait, Cross.ID) %>%
mutate_if(is.numeric, round, 3)
outliers %>%
kable("html", digits = 3, caption = "Effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140)") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| ES.ID | Study.name | SE | cross | Reciprocal | trait | Cross.ID |
|---|---|---|---|---|---|---|
| ES320 | Musolf et al_2015 | 21.535 | spL | Inviable | Frequ Modulated Downsweep_FAC1_2 | musculus.3 |
| ES320 | Musolf et al_2015 | 21.470 | hybSL | Inviable | Frequ Modulated Downsweep_FAC1_2 | musculus.3 |
| ES253 | Musolf et al_2015 | 14.218 | hybLS | Inviable | Complex 2_FAC2_1 | musculus.3 |
| ES253 | Musolf et al_2015 | 9.883 | spL | Inviable | Complex 2_FAC2_1 | musculus.3 |
| ES147 | Musolf et al_2015 | 6.075 | spL | Inviable | U Shaped Inverted_FAC1_2 | musculus.1 |
| ES123 | Musolf et al_2015 | 2.073 | hybLS | Inviable | Complex 2_FAC2_1 | musculus.1 |
| ES367 | Suvanto et al_1994 | 1.761 | hybLS | Viable | inhbt_IPI | Drosophila_borealis_Drosophila_virilis |
| ES371 | Suvanto et al_1994 | 1.538 | spL | Inviable | inhbt_PL | Drosophila_montana_Drosophila_virilis |
| ES367 | Suvanto et al_1994 | 1.375 | hybSL | Viable | inhbt_IPI | Drosophila_borealis_Drosophila_virilis |
| ES125 | Musolf et al_2015 | 1.276 | spL | Inviable | Complex 2_FAC1_4 | musculus.1 |
| ES123 | Musolf et al_2015 | 1.231 | spL | Inviable | Complex 2_FAC2_1 | musculus.1 |
| ES388 | Tomaru&Oguma_1994 | 0.007 | hybSL | Inviable | IPI | Drosophila_triauraria_Drosophila_biauraria |
| ES389 | Tomaru&Oguma_1994 | 0.006 | spL | Viable | IPI | Drosophila_quadraria_Drosophila_biauraria |
| ES055 | Coyne.et.al_1991 | 0.006 | hybSL | Viable | Tibia length | pn_j_irX85 |
| ES036 | Coyne_1983 | 0.006 | spL | Inviable | WL | BGxMutant |
| ES039 | Coyne_1983 | 0.006 | hybLS | Viable | WL | BGxSPD(wildtype) |
| ES036 | Coyne_1983 | 0.006 | hybLS | Inviable | WL | BGxMutant |
| ES390 | Tomaru&Oguma_1994 | 0.006 | spL | Inviable | IPI | Drosophila_quadraria_Drosophila_subauraria |
| ES039 | Coyne_1983 | 0.006 | hybSL | Viable | WL | BGxSPD(wildtype) |
| ES039 | Coyne_1983 | 0.006 | spL | Viable | WL | BGxSPD(wildtype) |
| ES388 | Tomaru&Oguma_1994 | 0.006 | spL | Inviable | IPI | Drosophila_triauraria_Drosophila_biauraria |
| ES387 | Tomaru&Oguma_1994 | 0.005 | spL | Viable | IPI | Drosophila_triauraria_Drosophila_quadraria |
| ES391 | Tomaru&Oguma_1994 | 0.005 | spL | Viable | IPI | Drosophila_biauraria_Drosophila_subauraria |
| ES390 | Tomaru&Oguma_1994 | 0.005 | hybLS | Inviable | IPI | Drosophila_quadraria_Drosophila_subauraria |
# load spLS files
dat <- read.csv("../data/dat.mean.csv", head = TRUE)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between larger species (spL) and hybrids
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("spL")) { # identify type of hybrids
for (cr in c("hybLS", "hybSL")) { # identify type of parentals
assign(
paste(cr, i, sep = "_"),
## lnRR ###
escalc(
measure = "ROM",
# N of hybrids
n1i = dat[, paste("N", i, sep = ".")],
# N of parentals
n2i = dat[, paste("N", cr, sep = ".")],
# Mean of hybrids
m1i = dat[, paste("Mn", i, sep = ".")],
# Mean of parentals
m2i = dat[, paste("Mn", cr, sep = ".")],
# SD of hybrids
sd1i = dat[, paste("SD", i, sep = ".")],
# SD of parentals
sd2i = dat[, paste("SD", cr, sep = ".")],
data = dat
) %>%
rename(lnRR.es = yi, lnRR.sv = vi) %>%
select(ES.ID, contains("lnRR")) %>%
mutate(
# direction of transgressve segregation - greater than parent
direction = "+",
# SE for logistic distribution, for the weighted binomial regression
# SE = pi/sqrt(3*(n_e + n_c))
SE = pi/sqrt(3*(
dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
))
)
)
}
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between hybrids and smaller species (spS)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("hybLS", "hybSL")) { # identify type of hybrids
for (cr in c("spS")) { # identify type of parentals
assign(
paste(i, cr, sep = "_"),
## lnRR ###
escalc(
measure = "ROM",
# N of hybrids
n1i = dat[, paste("N", i, sep = ".")],
# N of parentals
n2i = dat[, paste("N", cr, sep = ".")],
# Mean of hybrids
m1i = dat[, paste("Mn", i, sep = ".")],
# Mean of parentals
m2i = dat[, paste("Mn", cr, sep = ".")],
# SD of hybrids
sd1i = dat[, paste("SD", i, sep = ".")],
# SD of parentals
sd2i = dat[, paste("SD", cr, sep = ".")],
data = dat
) %>%
rename(lnRR.es = yi, lnRR.sv = vi) %>%
select(ES.ID, contains("lnRR")) %>%
mutate(
# direction of transgressve segregation - smaller than parent
direction = "-",
# SE for logistic distribution, for the weighted binomial regression
# SE = pi/sqrt(3*(n_e + n_c))
SE = pi/sqrt(3*(
dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
))
)
)
}
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Data output
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(
## Difference with mother species ###
bind_rows(hybLS_spL, hybSL_spS) %>%
mutate(parental = "Mother"),
## Difference with father species ###
bind_rows(hybLS_spS, hybSL_spL) %>%
mutate(parental = "Father")
) %>%
drop_na(lnRR.sv) %>%
left_join(., dat) %>%
# Assign novel phenotype to 1, non-novel phenotype to 0 for each effect size
mutate(Novelty = ifelse(lnRR.es < 0, 1, 0)) %>%
## Join with metadata ###
left_join(
., read.xlsx("../data/original.data.xlsx", sheet = "Species.level.moderators")
) %>%
mutate_at(vars(contains("divergence")), as.numeric) %>%
mutate_at(vars(contains("divergence")), scale) %>%
as.data.frame %>%
select(-trait) %>%
mutate_at("taxa", as.factor) %>%
within(levels(Novelty) <- c("No", "Novel phenotypic means")) %>%
within(
taxa <- ordered(
taxa,
levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
)
) %>%
# Remove rows without any metadata
drop_na(Hetero.sex, Pheno.divergence, trait.type, Genet.divergence) %>%
# Insect or no
mutate(
insect = ifelse(taxa %in% c("Aves", "Rodentia", "Anura", "Cichliformes"), "no", "insect"),
) %>%
write.csv("../data/mean.ES.Novelty.csv", row.names = F)
# Load effect sizes
mean.dif <- read.csv("../data/mean.ES.general.csv", head = TRUE) %>%
# indicate dataset including novel phenotype
mutate(data.type = "Alltraits")
# All trait observation irrespective of phenotypic novelty
All <- read.csv("../data/mean.ES.Novelty.csv", head = TRUE) %>%
filter(lnRR.es < 0) %>%
distinct(ES.ID)
# Trait observation of non-novel phenotypes
Nonovel <- read.csv("../data/mean.ES.general.csv", head = TRUE) %>%
filter(!(ES.ID %in% All$ES.ID)) %>%
# indicate dataset without novel phenotype
mutate(data.type = "Nonnoveltraits")
# combine dataset with/without novel phenotype
Novel.Nonnovel <- bind_rows(mean.dif, Nonovel) %>%
mutate(metaunit = str_c(data.type, cross, sep = "_"))%>%
# Use observations with both reciprocal crosses
drop_na(contains("Mn"), contains("SD")) %>%
# Insect or no
mutate(
insect = ifelse(taxa %in% c("Cichliformes", "Anura", "Aves", "Rodentia"), "no", "insect"),
)
#+++++++++++++++++++++++++++++++++++++++++++++++#
# Phylogeny
#+++++++++++++++++++++++++++++++++++++++++++++++#
# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
# names = levels(mean.dif$spL.name) %>%
# str_replace_all("_", " "),
# context_name = "Animals"
# )
#
# # which names return more than 1 match?
# inspect(taxa, ott_id = taxa$ott_id[taxa$number_matches != 1])
#
# # fixing names with more than 1 match
# taxa[taxa$number_matches != 1, ] <-
# inspect(taxa, ott_id = taxa$ott_id[taxa$number_matches != 1])[1, ]
#
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
# strip_ott_ids(remove_underscores=TRUE)
#
# print("Original tree from OTL")
# plot(tree, no.margin=TRUE)
#
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
# print("Randomly solved phylogeny")
# plot(bin.tree)
#
# # correlation matrix to fit to the model
# bin.tree$tip.label <- bin.tree$tip.label %>%
# as.factor()
#
# levels(bin.tree$tip.label) <- levels(mean.dif$spL.name) # making sure names match
# bin.tree$tip.label <- as.character(bin.tree$tip.label) # converting names back to character
#
# write.tree(bin.tree, file= "../data/phylo.mean.tre")
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.mean.tre") %>%
compute.brlen(bin.tree, method = "Grafen", power = 1)
# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)
# Note one of the tips is called "Lates calcarifer (estimated)"
phylo_branch$node.label <- NULL
# Plot tree
plot.phylo(phylo_branch, cex = 0.7)
Figure S1. Phylogenetic tree used in meta-analysis for phenotypic mean, which was based on tree of the parental species with larger phenotypic mean
Phylogenetic tree created based on taxonomic tree on Open Tree of Life. Non-binary tree was randomly solved. We used packages rotl, ape and phytools on R.
Compare midparent and hybrids by comparing difference from spSSM phenotypic mean to midparent-value \(\frac{spLL_M+spSS_M}{2}\) and to hybrids’ phenotypic mean.
As the variance of \(\ln\frac{spLL_M+spSS_M}{2spSS_M}\), we use the variance of \(\ln\frac{spLL_M}{spSS_M}\) for simplicity
We statistically compared those effect sizes through the formal meta-regression by using rma.mv function of R package metafor
All meta-analytic models included following Random effects estimates:
Study.ID: Primary studies. Denoted as Study
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations. Denoted as crossed strain
spL.name: Phylogeny of parental species (spLLM). Denoted as species with phylogeny
#++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Calculate midparent
#++++++++++++++++++++++++++++++++++++++++++++++++++++#
midparent <- Novel.Nonnovel %>%
# Divide mean by 2 in spL whereas hybrids are constant
mutate(
lnRR.midparent.es =
ifelse(
cross == "spL",
log(((Mn.spL + Mn.spS)/2)/Mn.spS), lnRR.es
)
# Divide variance by 4 in spL whereas hybrids are constant
) %>%
mutate_at("cross", as.factor) %>%
within(levels(cross) <- c("hybLS", "hybSL", "midparent"))
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
# Compare midparent and hybrids by meta-regresson
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- midparent %>%
filter(data.type == tr)
} else {
dat <- midparent %>%
filter(data.type == tr, insect == taxon)
}
# ## phylogenetic random regresson comparing with midparent ###
# midparent.compare <- rma.mv(
# yi = lnRR.midparent.es,
# V = lnRR.sv,
# data = dat,
# method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ relevel(cross, ref = "midparent")
# )
# ## Save model ###
# saveRDS(
# midparent.compare,
# file = paste("../Analysis/Mean.midparent.compare", tr, taxon, "obj", sep = ".")
# )
## Load model ###
midparent.compare <- readRDS(
paste("../Analysis/Mean.midparent.compare", tr, taxon, "obj", sep = ".")
)
assign(
paste("Dominance", tr, taxon, sep = "."),
midparent.compare %>%
get_reg() %>%
# Show dataset name
within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
# Rename fixed effects
within('Fixed effects' <- c("", "midparent (intrcpt)", "hybLS", "hybSL")) %>%
# Difference of each cross from midparent in %
mutate('Comparison with midparent' = c(
rep("", 2),
paste(
round(
100*(exp(midparent.compare$beta[1]+midparent.compare$beta[2]) -exp(midparent.compare$beta[1])), # hybrid LS
2),
"% larger"
),
paste(
round(
100*(exp(midparent.compare$beta[1]+midparent.compare$beta[3])-exp(midparent.compare$beta[1])), # hybrid SL
2),
"% larger"
)
))
)
# ## Meta-analysis for midparent to plot band ###
# midparent.random <- rma.mv(
# yi = lnRR.midparent.es,
# V = lnRR.sv,
# data = dat %>% filter(cross == "midparent"),
# method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor)
# )
# ## Save model ###
# saveRDS(
# midparent.random,
# file = paste("../Analysis/Mean.midparent", tr, taxon, "obj", sep = ".")
# )
}
}
bind_rows(
Dominance.Alltraits.alltaxon, Dominance.Nonnoveltraits.alltaxon,
Dominance.Alltraits.insect, Dominance.Nonnoveltraits.insect
) %>%
kable("html", digits = 3, caption = "Table S3. Results of meta-analysis investigating dominance in phenotypic means using full dataset or data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | Comparison with midparent |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | 99.7% | 38.8% | 1.1% | 0% | 59.8% | |||||||
| midparent (intrcpt) | 0.578 | 0.266 | 0.890 | 0 |
|
|||||||
| hybLS | -0.073 | -0.079 | -0.067 | 0 |
|
-12.53 % larger | ||||||
| hybSL | -0.100 | -0.106 | -0.093 | 0 |
|
-16.9 % larger | ||||||
| Nonnoveltraits alltaxon | 99.7% | 34.9% | 1.3% | 0% | 63.5% | |||||||
| midparent (intrcpt) | 0.677 | 0.370 | 0.984 | 0 |
|
|||||||
| hybLS | -0.142 | -0.149 | -0.134 | 0 |
|
-26 % larger | ||||||
| hybSL | -0.189 | -0.196 | -0.181 | 0 |
|
-33.84 % larger | ||||||
| Alltraits insect | 99.6% | 0% | 10% | 0% | 89.6% | |||||||
| midparent (intrcpt) | 0.396 | 0.306 | 0.486 | 0 |
|
|||||||
| hybLS | -0.073 | -0.079 | -0.066 | 0 |
|
-10.42 % larger | ||||||
| hybSL | -0.100 | -0.106 | -0.094 | 0 |
|
-14.15 % larger | ||||||
| Nonnoveltraits insect | 99.5% | 0% | 9.1% | 0% | 90.4% | |||||||
| midparent (intrcpt) | 0.494 | 0.395 | 0.594 | 0 |
|
|||||||
| hybLS | -0.142 | -0.150 | -0.134 | 0 |
|
-21.69 % larger | ||||||
| hybSL | -0.190 | -0.198 | -0.182 | 0 |
|
-28.36 % larger |
Quantify and visualise overall trend of phenotypic mean of hybrids compared to parents
Meta-analysis was repeated for subset data - including only non-novel phenotypes, only insects, and non-novel phenotypes in insects
Meta-analysis was conducted by using rma.mv function in metafor package
*Confidential interval : thick line
*Prediction interval : thin line
cap <- data.frame(
Description = c(
"Orchard plot for full data \n(Alltraits alltaxon)",
"Orchard plot for subset data:\n Excluding vertebrates \n(Alltraits insect)",
"Orchard plot for subset data:\n Including all taxon but excluding novel phenotype \n(Nonnoveltraits alltaxon)",
"Orchard plot for subset data:\n Excluding vertebrates and novel phenotype\n (Nonnoveltraits insect)"
)
) %>%
mutate_all(as.character)
row.names(cap) <- c("Alltraits.alltaxon", "Alltraits.insect",
"Nonnoveltraits.alltaxon", "Nonnoveltraits.insect")
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- Novel.Nonnovel %>%
filter(cross == cr, data.type == tr)
} else {
dat <- Novel.Nonnovel %>%
filter(cross == cr, data.type == tr, insect == taxon)
}
res.ma <- foreach(
cr = c("spL", "hybLS", "hybSL"), .combine = `rbind`
) %do% {
# # Phylogenetic random meta-analysis
# phyl.random <- rma.mv(
# yi = lnRR.es, V = lnRR.sv,
# data = dat, method = "REML",
# random = list(
# ~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID
# ),
# R = list(spL.name = phylo_cor)
# )
# saveRDS(phyl.random,
# file = paste("../Analysis/Mean.meta", tr, taxon, cr, "obj", sep = "."))
# creating summary table of results
readRDS(
paste("../Analysis/Mean.meta", tr, taxon, cr, "obj", sep = ".")
) %>%
get_pred() %>%
as.data.frame() %>%
mutate(
N = length(dat$ES.ID),
# I2 = I2(phyl.random)["I2_total"] %>% round(3),
cross = cr # indicate cross (spL, hybLS, hybSL)
) %>%
as.data.frame() %>%
within('Dataset' <- c(paste(tr, taxon), "", "")) %>% # dataset name in first row
return()
}
if (taxon == "alltaxon") {
dat.plot <- Novel.Nonnovel %>%
filter(data.type == tr)
} else {
dat.plot <- Novel.Nonnovel %>%
filter(data.type == tr, insect == taxon)
}
# Bind result of all crosses at each dataset
assign(
paste("res.ma", tr, taxon, sep = "."),
res.ma
)
## Midparent estimate & CI ###
midparent.ma.2 <- readRDS(
paste("../Analysis/Mean.midparent", tr, taxon, "obj", sep = ".")
) %>%
# Prediction interval of midparent
get_pred() %>%
as.data.frame()
# creating a forest plot
plot.metamean <- ggplot(
data = res.ma,
aes(x = tanh(Estimate), y = cross)
) +
scale_y_discrete(expand = c(0, 1)) +
scale_x_continuous(
limits = c(-1, 1),
breaks = seq(-1, 1, by = 0.5)
) +
## Midparent CI ###
geom_rect(
aes(
xmin = tanh(midparent.ma.2$LowerCI),
xmax = tanh(midparent.ma.2$UpperCI),
ymin = -Inf, ymax = Inf
),
fill="Grey 90"
) +
## Midparent estimate ###
geom_vline(
xintercept = tanh(midparent.ma.2$Estimate), colour = "grey20",
alpha = 0.3, size = 1
) +
## Orchard plot ###
geom_quasirandom(
data = dat.plot,
aes(x = tanh(lnRR.es), y = cross, size = 1/lnRR.sv, color = cross),
alpha = 0.2, GroupOnX = FALSE
) +
scale_color_manual(values = c("#DC267F", "#785EF0", "#FE6100")) +
# Delete legend for colors
guides(fill = "none", colour = "none") +
# Confidential interval : thicker line
geom_errorbarh(
aes(xmin = tanh(LowerCI), xmax = tanh(UpperCI)),
height = 0, size = 1.2, alpha = 0.6
) +
# Prediction interval : thinner line
geom_errorbarh(
aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)),
height = 0.1, size = 0.5, alpha = 0.6
) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
## Estimate ###
geom_point(size = 3, shape = 21, fill = "black") +
# ## I^2 ###
# annotate(
# "text", x = -0.93, y = c(1.35, 2.35, 3.35),
# label = paste("italic(I^2)==", res.ma$I2),
# parse = TRUE, hjust = "left", size = 5
# ) +
## N ###
annotate(
"text", x = -0.93, y = 3.85,
label = paste("italic(N)==", length(dat.plot$ES.ID)/3),
parse = TRUE, hjust = "left", size = 5
) +
annotate(
"text", x = -0.68, y = 3.85,
label = "each", hjust = "left", size = 5
) +
ylab("") +
xlab(expression(
paste("hyperbolic tangent of lnRR from ", italic("spSS")),
parse = TRUE
)) +
ggtitle(cap[paste(tr, taxon,sep="."),]) +
orchardtheme
print(plot.metamean)
# ggsave(
# plot = plot.metamean,
# file = paste("../Analysis/mean.general.metamean", tr, taxon, "svg", sep = "."),
# height = 4, width = 5
# )
}
}
bind_rows(
res.ma.Alltraits.alltaxon, res.ma.Nonnoveltraits.alltaxon,
res.ma.Alltraits.insect, res.ma.Nonnoveltraits.insect
) %>%
as.data.frame() %>%
# mutate_at(vars('Estimate':'%I2[residual]'), as.numeric) %>%
# select(Dataset, cross, Estimate, UpperCI, LowerCI, contains("PR"), contains("I2")) %>%
kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | lowerPR | upperPR | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | N | cross |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | intrcpt | 0.837 | 0.411 | 1.263 | -0.552 | 2.226 | 0.000 | 99.9% | 28.2% | 7.3% | 0% | 64.4% |
|
0 | spL |
| Alltraits alltaxon | intrcpt | 0.477 | 0.246 | 0.708 | -0.428 | 1.383 | 0.000 | 99.7% | 16.8% | 2.2% | 0% | 80.7% |
|
0 | hybLS |
| Alltraits alltaxon | intrcpt | 0.425 | 0.178 | 0.672 | -0.587 | 1.437 | 0.001 | 99.8% | 14.9% | 2.5% | 0% | 82.4% |
|
0 | hybSL |
| Nonnoveltraits alltaxon | intrcpt | 0.983 | 0.555 | 1.411 | -0.425 | 2.391 | 0.000 | 99.9% | 24.5% | 16.1% | 0% | 59.3% |
|
79 | spL |
| Nonnoveltraits alltaxon | intrcpt | 0.482 | 0.257 | 0.708 | -0.316 | 1.280 | 0.000 | 99.6% | 21% | 0% | 0% | 78.6% |
|
79 | hybLS |
| Nonnoveltraits alltaxon | intrcpt | 0.421 | 0.210 | 0.633 | -0.299 | 1.142 | 0.000 | 99.4% | 22.3% | 4.2% | 0% | 73% |
|
79 | hybSL |
| Alltraits insect | intrcpt | 0.604 | 0.446 | 0.762 | -0.486 | 1.693 | 0.000 | 99.9% | 0% | 12.3% | 0% | 87.6% |
|
92 | spL |
| Alltraits insect | intrcpt | 0.362 | 0.265 | 0.460 | -0.411 | 1.135 | 0.000 | 99.6% | 0% | 3.8% | 0% | 95.9% |
|
92 | hybLS |
| Alltraits insect | intrcpt | 0.298 | 0.193 | 0.402 | -0.583 | 1.178 | 0.000 | 99.7% | 0% | 2.1% | 0% | 97.6% |
|
92 | hybSL |
| Nonnoveltraits insect | intrcpt | 0.741 | 0.541 | 0.940 | -0.384 | 1.866 | 0.000 | 99.8% | 0% | 23.3% | 0% | 76.6% |
|
72 | spL |
| Nonnoveltraits insect | intrcpt | 0.367 | 0.280 | 0.455 | -0.271 | 1.005 | 0.000 | 99.4% | 0% | 2% | 0% | 97.4% |
|
72 | hybLS |
| Nonnoveltraits insect | intrcpt | 0.313 | 0.225 | 0.401 | -0.243 | 0.869 | 0.000 | 99.2% | 0% | 9.4% | 0% | 89.8% |
|
72 | hybSL |
We statistically compared lnRR from spSSM to the other crosses through the formal meta-regression by using rma.mv function of R package metafor.
Here we asked if lnRR of hybSLM was smaller/larger than that of hybLSM
If \(\ln\frac{hybridLS_M}{spSS_M}>\ln\frac{hybridSL_M}{spSS_M}\), hybridLSM has larger phenotypic mean than hybridSLM …maternal inheritance
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- Novel.Nonnovel %>%
filter(data.type == tr)
} else {
dat <- Novel.Nonnovel %>%
filter(data.type == tr, insect == taxon)
}
# #+++++++++++++++++++++++++++++++++++++++++++++#
# # phylogenetic random regresson (ANOVA)
# #+++++++++++++++++++++++++++++++++++++++++++++#
# # Regress by lnRR of phenotypic difference between parentals
# phyl.random.spL <- rma.mv(
# yi = lnRR.es, V = lnRR.sv,
# data = dat, method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ relevel(cross, ref = "spL")
# )
# phyl.random.hybLS <- rma.mv(
# yi = lnRR.es, V = lnRR.sv,
# data = dat, method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ cross
# )
# # Save model ###
# saveRDS(
# phyl.random.spL,
# file = paste("../Analysis/Mean.compare.from.spL", tr, taxon, "obj", sep = ".")
# )
# saveRDS(
# phyl.random.hybLS,
# file = paste("../Analysis/Mean.compare.from.hybLS", tr, taxon, "obj", sep = ".")
# )
phyl.random.hybLS <- readRDS(
paste("../Analysis/Mean.compare.from.hybLS", tr, taxon, "obj", sep = ".")
)
assign(
paste("Reciprocal", tr, taxon, sep = "."),
phyl.random.hybLS %>%
get_reg() %>%
# Show dataset name
within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
# Rename fixed effects
within('Fixed effects' <- c("", "hybLS (intrcpt)", "hybSL", "spLL")) %>%
# Difference of each cross from midparent in %
mutate('Comparison with hybridLS' = c(
rep("", 2),
paste(
round(
(exp(phyl.random.hybLS$beta[1]+phyl.random.hybLS$beta[2]) -
exp(phyl.random.hybLS$beta[1])
)*100,
2),
"% larger"
),
""
)
)
)
}
}
bind_rows(
Reciprocal.Alltraits.alltaxon, Reciprocal.Nonnoveltraits.alltaxon,
Reciprocal.Alltraits.insect, Reciprocal.Nonnoveltraits.insect
) %>%
kable("html", digits = 3, caption = "Table S4. Results of meta-analysis investigating crossing direction effects in phenotypic means using full dataset or data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | Comparison with hybridLS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | 99.8% | 39.1% | 2.9% | 0% | 57.9% | |||||||
| hybLS (intrcpt) | 0.566 | 0.193 | 0.939 | 0.003 |
|
|||||||
| hybSL | -0.028 | -0.035 | -0.022 | 0.000 |
|
-4.94 % larger | ||||||
| spLL | 0.240 | 0.233 | 0.246 | 0.000 |
|
|||||||
| Nonnoveltraits alltaxon | 99.8% | 35.7% | 2.2% | 0% | 61.9% | |||||||
| hybLS (intrcpt) | 0.589 | 0.223 | 0.954 | 0.002 |
|
|||||||
| hybSL | -0.049 | -0.057 | -0.041 | 0.000 |
|
-8.67 % larger | ||||||
| spLL | 0.368 | 0.360 | 0.376 | 0.000 |
|
|||||||
| Alltraits insect | 99.7% | 0% | 11.9% | 0% | 87.8% | |||||||
| hybLS (intrcpt) | 0.349 | 0.239 | 0.460 | 0.000 |
|
|||||||
| hybSL | -0.029 | -0.036 | -0.022 | 0.000 |
|
-4.08 % larger | ||||||
| spLL | 0.239 | 0.233 | 0.245 | 0.000 |
|
|||||||
| Nonnoveltraits insect | 99.6% | 0% | 9.7% | 0% | 89.9% | |||||||
| hybLS (intrcpt) | 0.365 | 0.246 | 0.484 | 0.000 |
|
|||||||
| hybSL | -0.051 | -0.059 | -0.043 | 0.000 |
|
-7.12 % larger | ||||||
| spLL | 0.368 | 0.361 | 0.376 | 0.000 |
|
We created data subset according to taxon (all taxon or insect only) and novelty in phenotypic variabilities (all traits or non-novel traits - trait variability [in terms of coefficient of variation] of hybrids does not exceed size range of parents). We conduct identical analyses for full data and data subsets, and checked robustness of our results.
We quantified relative phenotypic variability of hybrids and the parent with large variability (spLLV) to the parent with small variability (spSSV), by using [lnCVR] (https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12309) (Nakagawa et al. (2015)). We controlled mean-variance relationship by using Coefficient of Variation (CV) as the proxy of phenotypic variability.
Effecct size was calculated by using escalc function in metafor package
# load spLS files
dat <- read.csv("../data/dat.CV.csv", head = TRUE)
# Order by taxon
dat$taxa <- ordered(
dat$taxa,
levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
)
var.dif <- foreach(
cr = c("hybLS", "hybSL", "spL"),
.combine = `rbind`
) %do% {
escalc(
measure = "CVR",
# N of hybrids
n1i = dat[, paste("N", cr, sep = ".")],
# N of parentals
n2i = dat[, "N.spS"],
# Mean of hybrids
m1i = dat[, paste("Mn", cr, sep = ".")],
# Mean of parentals
m2i = dat[, "Mn.spS"],
# SD of hybrids
sd1i = dat[, paste("SD", cr, sep = ".")],
# SD of parentals
sd2i = dat[, "SD.spS"],
data = dat
) %>%
rename(lnCVR.es = yi, lnCVR.sv = vi) %>%
mutate(cross = cr)
} %>%
drop_na(lnCVR.es) %>%
arrange(ES.ID) %>%
left_join(., dat)
res <- rma(yi = lnCVR.es, vi = lnCVR.sv, data = var.dif, method="FE")
## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-5, 5))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-5, 5))
Checked effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 8.5) and confirmed that those data were correctly imported.
outliers <- var.dif %>%
# vi (sampling variance)
mutate(SE = sqrt(lnCVR.sv)) %>%
arrange(desc(SE)) %>%
filter(SE > 1.2 | 1/SE > 8.5) %>%
select(ES.ID, Study.name, SE, cross, Reciprocal, trait, Cross.ID) %>%
mutate_if(is.numeric, round, 3)
outliers %>%
kable("html", digits = 3, caption = "Effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140)") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| ES.ID | Study.name | SE | cross | Reciprocal | trait | Cross.ID |
|---|---|---|---|---|---|---|
| ES320 | Musolf et al_2015 | 21.540 | spL | Inviable | Frequ Modulated Downsweep_FAC1_2 | musculus.3 |
| ES253 | Musolf et al_2015 | 10.257 | hybSL | Inviable | Complex 2_FAC2_1 | musculus.3 |
| ES253 | Musolf et al_2015 | 9.917 | spL | Inviable | Complex 2_FAC2_1 | musculus.3 |
| ES147 | Musolf et al_2015 | 6.096 | spL | Inviable | U Shaped Inverted_FAC1_2 | musculus.1 |
| ES123 | Musolf et al_2015 | 1.974 | hybSL | Inviable | Complex 2_FAC2_1 | musculus.1 |
| ES320 | Musolf et al_2015 | 1.933 | hybLS | Inviable | Frequ Modulated Downsweep_FAC1_2 | musculus.3 |
| ES367 | Suvanto et al_1994 | 1.656 | hybSL | Viable | inhbt_IPI | Drosophila_borealis_Drosophila_virilis |
| ES371 | Suvanto et al_1994 | 1.617 | spL | Inviable | inhbt_PL | Drosophila_montana_Drosophila_virilis |
| ES125 | Musolf et al_2015 | 1.376 | spL | Inviable | Complex 2_FAC1_4 | musculus.1 |
| ES123 | Musolf et al_2015 | 1.334 | spL | Inviable | Complex 2_FAC2_1 | musculus.1 |
| ES254 | Musolf et al_2015 | 1.265 | spL | Inviable | Complex 2_FAC1_3 | musculus.3 |
| ES246 | Musolf et al_2015 | 1.216 | spL | Inviable | Complex 2_bandwcentre | musculus.3 |
| ES367 | Suvanto et al_1994 | 1.205 | spL | Viable | inhbt_IPI | Drosophila_borealis_Drosophila_virilis |
| ES388 | Tomaru&Oguma_1994 | 0.104 | hybLS | Inviable | IPI | Drosophila_triauraria_Drosophila_biauraria |
| ES041 | Coyne_1985 | 0.087 | hybSL | Viable | Sex comb tooth number | BGxOxnard |
| ES387 | Tomaru&Oguma_1994 | 0.076 | spL | Viable | IPI | Drosophila_triauraria_Drosophila_quadraria |
| ES390 | Tomaru&Oguma_1994 | 0.075 | hybLS | Inviable | IPI | Drosophila_quadraria_Drosophila_subauraria |
| ES390 | Tomaru&Oguma_1994 | 0.073 | spL | Inviable | IPI | Drosophila_quadraria_Drosophila_subauraria |
| ES041 | Coyne_1985 | 0.071 | spL | Viable | Sex comb tooth number | BGxOxnard |
| ES041 | Coyne_1985 | 0.071 | hybLS | Viable | Sex comb tooth number | BGxOxnard |
| ES389 | Tomaru&Oguma_1994 | 0.071 | spL | Viable | IPI | Drosophila_quadraria_Drosophila_biauraria |
| ES388 | Tomaru&Oguma_1994 | 0.064 | spL | Inviable | IPI | Drosophila_triauraria_Drosophila_biauraria |
| ES391 | Tomaru&Oguma_1994 | 0.060 | spL | Viable | IPI | Drosophila_biauraria_Drosophila_subauraria |
var.dif.2 <- var.dif %>%
filter(!ES.ID %in% unique(outliers$ES.ID))
write.csv(var.dif.2, "../data/variation.ES.general.csv", row.names = F)
Because they inhibit meta-analytic model convergence, we excluded these outliers from the formal meta-analysis dataset, but not from novel variability dataset.
Funnel plots after outliers excluded
res <- rma(yi = lnCVR.es, vi = lnCVR.sv, data = var.dif.2, method="FE")
## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-5, 5))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-5, 5))
# load spLS files
dat <- read.csv("../data/dat.CV.csv", head = TRUE)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between larger species (spL) and hybrids
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("spL")) { # identify type of hybrids
for (cr in c("hybLS", "hybSL")) { # identify type of parentals
assign(
paste(cr, i, sep = "_"),
escalc(
measure = "CVR",
# N of hybrids
n1i = dat[, paste("N", i, sep = ".")],
# N of parentals
n2i = dat[, paste("N", cr, sep = ".")],
# Mean of hybrids
m1i = dat[, paste("Mn", i, sep = ".")],
# Mean of parentals
m2i = dat[, paste("Mn", cr, sep = ".")],
# SD of hybrids
sd1i = dat[, paste("SD", i, sep = ".")],
# SD of parentals
sd2i = dat[, paste("SD", cr, sep = ".")],
data = dat
) %>%
select(ES.ID, yi, vi) %>%
rename("lnCVR.es" = "yi", "lnCVR.sv" = "vi") %>%
mutate(
# direction of transgressve segregation - greater than parent
direction = "+",
# SE for logistic distribution, for the weighted binomial regression
# SE = pi/sqrt(3*(n_e + n_c))
SE = pi/sqrt(3*(
dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
))
)
)
}
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between hybrids and smaller species (spS)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("hybLS", "hybSL")) { # identify type of hybrids
for (cr in c("spS")) { # identify type of parentals
assign(
paste(i, cr, sep = "_"),
escalc(
measure = "CVR",
# N of hybrids
n1i = dat[, paste("N", i, sep = ".")],
# N of parentals
n2i = dat[, paste("N", cr, sep = ".")],
# Mean of hybrids
m1i = dat[, paste("Mn", i, sep = ".")],
# Mean of parentals
m2i = dat[, paste("Mn", cr, sep = ".")],
# SD of hybrids
sd1i = dat[, paste("SD", i, sep = ".")],
# SD of parentals
sd2i = dat[, paste("SD", cr, sep = ".")],
data = dat
) %>%
select(ES.ID, yi, vi) %>%
rename("lnCVR.es" = "yi", "lnCVR.sv" = "vi") %>%
mutate(
# direction of transgressve segregation - smaller than parent
direction = "-",
# SE for logistic distribution, for the weighted binomial regression
# SE = pi/sqrt(3*(n_e + n_c))
SE = pi/sqrt(3*(
dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
))
)
)
}
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Data output
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(
## Difference with mother species ###
bind_rows(hybLS_spL, hybSL_spS) %>%
mutate(parental = "Mother"),
## Difference with father species ###
bind_rows(hybLS_spS, hybSL_spL) %>%
mutate(parental = "Father")
) %>%
drop_na(lnCVR.es) %>%
left_join(., dat) %>%
# Assign novel variability to 1, non-novel variability to 0 for each effect size
mutate(Novelty = ifelse(lnCVR.es < 0, 1, 0)) %>%
## Join with metadata ###
left_join(
.,
read.xlsx(
"../data/original.data.xlsx", sheet = "Species.level.moderators"
)
) %>%
mutate_at(vars(contains("divergence")), as.numeric) %>%
mutate_at(vars(contains("divergence")), scale) %>%
as.data.frame %>%
select(-trait) %>%
drop_na(Hetero.sex, trait.type, Genet.divergence) %>%
# order taxon
within(
taxa <- ordered(
taxa,
levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
)
) %>%
mutate_if(is.character, as.factor) %>%
# Insect or no
mutate(
insect = ifelse(taxa %in% c("Aves", "Rodentia", "Anura", "Cichliformes"), "no", "insect"),
) %>%
write.csv("../data/variation.ES.Novelty.csv", row.names = F)
# Load effect sizes
var.dif <- read.csv("../data/variation.ES.general.csv", head = TRUE)%>%
# indicate dataset including novel variability
mutate(data.type = "Alltraits")
# Observations with novel variability
All <- read.csv("../data/variation.ES.novelty.csv", head = TRUE) %>%
filter(lnCVR.es < 0) %>%
distinct(ES.ID)
# Effect sizes with non-novel variability
Nonovel <- read.csv("../data/variation.ES.general.csv", head = TRUE) %>%
filter(!ES.ID %in% unique(All$ES.ID)) %>%
# indicate dataset without novel variability
mutate(data.type = "Nonnoveltraits")
# combine dataset with/without novel variability
Novel.Nonnovel <- bind_rows(var.dif, Nonovel) %>%
mutate(metaunit = str_c(data.type, cross, sep = "_")) %>%
# Use observations with both reciprocal crosses
drop_na(contains("Mn"), contains("SD")) %>%
# Insect or no
mutate(
insect = ifelse(taxa %in% c("Cichliformes", "Anura", "Aves", "Rodentia"), "no", "insect"),
)
#+++++++++++++++++++++++++++++++#
# Phylogeny
#+++++++++++++++++++++++++++++++#
# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
# names = levels(var.dif$spL.name) %>%
# str_replace_all("_", " "),
# context_name = "Animals"
# )
#
# # Create tree
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
# strip_ott_ids # remove OTT IDs from tip labels
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
#
# # Fix names of tip labels
# bin.tree$tip.label %<>% str_replace_all("Dryophytes", "Hyla")
# # Indicate mismatch between tip labels & dataset species names
# setdiff(levels(as.factor(bin.tree$tip.label)), levels(var.dif$spL.name))
# setdiff(levels(var.dif$spL.name), levels(as.factor(bin.tree$tip.label)))
# write.tree(bin.tree, file= "../data/phylo.variation.tre")
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.variation.tre") %>%
compute.brlen(bin.tree, method = "Grafen", power = 1)
# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)
# Note one of the tips is called "Lates calcarifer (estimated)"
phylo_branch$node.label <- NULL
# Plot tree
plot.phylo(phylo_branch, cex = 0.7)
Figure S2. Phylogenetic tree used in meta-analysis for phenotypic variability (coefficient of variation, CV), which was based on tree of the parental species with larger CV in trait size
We compared midparent-value of phenotypic variability (CV) and hybrids’ phenotypic variability by calculating log ratio of phenotypic variability (lnCVR) of midparent-value and hybrids to spSSV.
lnCVR of midparent-value to spSSV was, following \(lnCVR=\ln{\frac{CV_E}{CV_C}}+\frac{1}{2(N_E-1)}-\frac{1}{2(N_C-1)}\) , calculated as
\[\ln\frac{CV_{spLL_V}+CV_{spSS_V}}{2}-\ln{CV_{spSS_V}} + \frac{1}{N_{spLL_V}+N_{spSS_V}-2}-\frac{1}{2(N_{spSS_V} - 1)}\] It’s sampling variance was substitute by that of spLLV
We statistically compared those effect sizes through the formal meta-regression by using rma.mv function of R package metafor
All meta-analytic models included following Random effects estimates:
Study.ID: Primary studies. Denoted as Study
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations. Denoted as crossed strain
spL.name: Phylogeny of parental species (spLLM). Denoted as species with phylogeny
#+++++++++++++++++++++++++++++++++++++++++++#
# Calculate midparent
#+++++++++++++++++++++++++++++++++++++++++++#
midparent <- Novel.Nonnovel %>%
# Divide mean by 2 in spL whereas hybrids are constant
mutate(
CV.spL = SD.spL/Mn.spL,
CV.spS = SD.spS/Mn.spS
) %>%
mutate(
lnCVR.midparent.es =
ifelse(
cross == "spL",
# log(CVe/CVc)
log((CV.spL + CV.spS)/2) - log(CV.spS) +
# 1/2(Ne-1) - 1/2(Nc-1)
1/(N.spL + N.spS -2) - 1/(2*N.spS - 2),
lnCVR.es
)
# Divide variance by 4 in spL whereas hybrids are constant
) %>%
mutate_at("cross", as.factor) %>%
within(levels(cross) <- c("hybLS", "hybSL", "midparent"))
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
# Compare midparent and hybrids by meta-regresson
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
# Filtering taxon (all taxon or insects)
if (taxon == "alltaxon") {
dat <- midparent %>%
filter(data.type == tr)
} else {
dat <- midparent %>%
filter(data.type == tr, insect == taxon)
}
# #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# # Estimate meta-analytic mean of midparent of phenotypic variation
# #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# midparent.compare <- rma.mv(
# yi = lnCVR.midparent.es, V = lnCVR.sv,
# data = dat,
# method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ relevel(cross, ref = "midparent")
# )
# ## Save model ###
# saveRDS(
# midparent.compare,
# file = paste("../Analysis/Variation.midparent.compare", tr, taxon, "obj", sep = ".")
# )
## Load model ###
midparent.compare <- readRDS(paste("../Analysis/Variation.midparent.compare", tr, taxon, "obj", sep = "."))
assign(
paste("Dominance", tr, taxon, sep = "."),
get_reg(midparent.compare) %>%
# Show dataset name
within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
# Rename fixed effects
within('Fixed effects' <- c("", "midparent (intrcpt)", "hybLS", "hybSL")) %>%
# Difference of each cross from midparent in %
mutate('Comparison with midparent' = c(
rep("", 2),
# Difference hybLS - midparent
paste(
round(
100*(exp(midparent.compare$beta[1]+midparent.compare$beta[2]) -exp(midparent.compare$beta[1])), # hybrid LS
2),
"% larger"
),
# beta[1] : midparent
# beta[2] : hybLS
# beta[3] : hybSL
# Difference hybSL - midparent
paste(
round(
100*(exp(midparent.compare$beta[1]+midparent.compare$beta[3])-exp(midparent.compare$beta[1])), # hybrid SL
2),
"% larger"
)
))
)
# #++++++++++++++++++++++++++++++++++++++++++++++#
# # Meta-analysis for midparent to plot band
# #++++++++++++++++++++++++++++++++++++++++++++++#
#
# # Phylogenetic random meta-analysis
# midparent.random <- rma.mv(
# yi = lnCVR.midparent.es,
# V = lnCVR.sv,
# data = dat %>%
# filter(cross == "midparent"),
# method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor)
# )
# ## Save model ###
# saveRDS(
# midparent.random,
# file = paste("../Analysis/Variation.midparent", tr, taxon, "obj", sep = ".")
# )
}
}
bind_rows(
Dominance.Alltraits.alltaxon, Dominance.Nonnoveltraits.alltaxon,
Dominance.Alltraits.insect, Dominance.Nonnoveltraits.insect
) %>%
kable("html", digits = 3, caption = "Table S5. Results of meta-analysis investigating dominance in phenotypic variabilities using full dataset or data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | Comparison with midparent |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | 75.6% | 19.5% | 11.4% | 0% | 44.7% | |||||||
| midparent (intrcpt) | 0.381 | 0.079 | 0.682 | 0.013 |
|
|||||||
| hybLS | -0.020 | -0.098 | 0.058 | 0.610 | -2.94 % larger | |||||||
| hybSL | -0.091 | -0.170 | -0.013 | 0.022 |
|
-12.77 % larger | ||||||
| Nonnoveltraits alltaxon | 69.9% | 14.4% | 21% | 0% | 34.5% | |||||||
| midparent (intrcpt) | 0.589 | 0.275 | 0.902 | 0.000 |
|
|||||||
| hybLS | -0.146 | -0.257 | -0.035 | 0.010 |
|
-24.49 % larger | ||||||
| hybSL | -0.144 | -0.254 | -0.033 | 0.011 |
|
-24.13 % larger | ||||||
| Alltraits insect | 67.8% | 0.1% | 15.3% | 0% | 52.4% | |||||||
| midparent (intrcpt) | 0.261 | 0.118 | 0.404 | 0.000 |
|
|||||||
| hybLS | -0.016 | -0.096 | 0.064 | 0.695 | -2.06 % larger | |||||||
| hybSL | -0.113 | -0.193 | -0.032 | 0.006 |
|
-13.82 % larger | ||||||
| Nonnoveltraits insect | 56.9% | 0% | 24.4% | 0% | 32.5% | |||||||
| midparent (intrcpt) | 0.452 | 0.259 | 0.645 | 0.000 |
|
|||||||
| hybLS | -0.144 | -0.258 | -0.030 | 0.013 |
|
-21.11 % larger | ||||||
| hybSL | -0.173 | -0.287 | -0.059 | 0.003 |
|
-24.94 % larger |
Quantify and visualise overall trend of phenotypic variability (CV) of hybrids compared to parents
Meta-analysis was repeated for subset data - including only non-novel phenotypes, only insects, and non-novel phenotypes in insects
Meta-analysis was conducted by using rma.mv function in metafor package
Confidential interval : thick line
Prediction interval : thin line
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- Novel.Nonnovel %>%
filter(cross == cr, data.type == tr)
} else {
dat <- Novel.Nonnovel %>%
filter(cross == cr, data.type == tr, insect == taxon)
}
res.ma <- foreach(
cr = c("spL", "hybLS", "hybSL"), .combine = `rbind`
) %do% {
# # Phylogenetic random meta-analysis
# phyl.random <- rma.mv(
# yi = lnCVR.es, V = lnCVR.sv,
# data = dat, method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor)
# )
# saveRDS(phyl.random,
# file = paste("../Analysis/Variation.meta", tr, taxon, cr, "obj", sep = "."))
# creating summary table of results
readRDS(
paste("../Analysis/Variation.meta", tr, taxon, cr, "obj", sep = ".")
) %>%
get_pred() %>%
as.data.frame() %>%
mutate(
N = length(dat$ES.ID),
# I2 = I2(phyl.random)["I2_total"] %>% round(3),
cross = cr # indicate cross (spL, hybLS, hybSL)
) %>%
as.data.frame() %>%
within('Dataset' <- c(paste(tr, taxon), "", "")) %>% # dataset name in first row
return()
}
if (taxon == "alltaxon") {
dat.plot <- Novel.Nonnovel %>%
filter(data.type == tr)
} else {
dat.plot <- Novel.Nonnovel %>%
filter(data.type == tr, insect == taxon)
}
# Bind result of all crosses at each dataset
assign(
paste("res.ma", tr, taxon, sep = "."),
res.ma
)
## Midparent estimate & CI ###
midparent.ma.2 <- readRDS(
paste("../Analysis/Variation.midparent", tr, taxon, "obj", sep = ".")
) %>%
# Prediction interval of midparent
get_pred() %>%
as.data.frame()
# creating a forest plot
plot.metamean <- ggplot(
data = res.ma,
aes(x = tanh(Estimate), y = cross)
) +
scale_y_discrete(expand = c(0,1)) +
scale_x_continuous(
limits = c(-1, 1),
breaks = seq(-1, 1, by = 0.5)
) +
## Midparent CI ###
geom_rect(
aes(
xmin = tanh(midparent.ma.2$LowerCI),
xmax = tanh(midparent.ma.2$UpperCI),
ymin = -Inf, ymax = Inf
),
fill="Grey 95", inherit.aes = FALSE
) +
## Midparent estimate ###
geom_vline(
xintercept = tanh(midparent.ma.2$Estimate), colour = "grey20",
alpha = 0.3, size = 1
) +
## Orchard plot ###
geom_quasirandom(
data = dat.plot %>%
filter(data.type == tr),
aes(x = tanh(lnCVR.es), y = cross, size = 1/lnCVR.sv, color = cross),
alpha = 0.2, GroupOnX = FALSE
) +
scale_color_manual(values = c("#DC267F", "#785EF0", "#FE6100")) +
# Delete legend for colors
guides(fill = "none", colour = "none") +
## Confidential interval ###
geom_errorbarh(
aes(xmin = tanh(LowerCI), xmax = tanh(UpperCI)),
height = 0, size = 1.2, alpha = 0.6
) +
## Prediction interval ###
geom_errorbarh(
aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)),
height = 0.1, size = 0.5, alpha = 0.6
) +
geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
## Estimate ###
geom_point(size = 3, shape = 21, fill = "black") +
## N ###
annotate(
"text", x = -0.93, y = 3.85,
label = paste("italic(N)==", length(dat.plot$ES.ID)/3),
parse = TRUE, hjust = "left", size = 5
) +
annotate(
"text", x = -0.68, y = 3.85,
label = "each", hjust = "left", size = 5
) +
ylab("") +
xlab(expression(
paste("hyperbolic tangent of lnCVR from ", italic("spSS")),
parse = TRUE
)) +
ggtitle(cap[paste(tr, taxon,sep="."),]) +
orchardtheme
print(plot.metamean)
# ggsave(
# plot = plot.metamean,
# file = paste("../Analysis/variation.general.metamean", tr, taxon, "svg", sep = "."),
# height = 4, width = 5
# )
}
}
bind_rows(
res.ma.Alltraits.alltaxon, res.ma.Nonnoveltraits.alltaxon,
res.ma.Alltraits.insect, res.ma.Nonnoveltraits.insect
) %>%
as.data.frame() %>%
select(Dataset, cross, Estimate, UpperCI, LowerCI, contains("PR"), contains("I2")) %>%
kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | cross | Estimate | UpperCI | LowerCI | lowerPR | upperPR | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | spL | 0.538 | 0.763 | 0.314 | -0.240 | 1.317 | 68.9% | 12.7% | 12.1% | 0% | 44.1% |
| Alltraits alltaxon | hybLS | 0.302 | 0.531 | 0.073 | -0.913 | 1.516 | 82.4% | 3.2% | 10.8% | 0% | 68.4% |
| Alltraits alltaxon | hybSL | 0.345 | 0.790 | -0.101 | -1.048 | 1.738 | 84.8% | 24.5% | 18.4% | 0% | 41.8% |
| Nonnoveltraits alltaxon | spL | 0.734 | 0.983 | 0.485 | -0.265 | 1.733 | 77.2% | 0% | 29.1% | 0% | 48.1% |
| Nonnoveltraits alltaxon | hybLS | 0.402 | 0.646 | 0.157 | -0.567 | 1.371 | 69.6% | 0% | 24% | 0% | 45.7% |
| Nonnoveltraits alltaxon | hybSL | 0.556 | 1.161 | -0.049 | -0.722 | 1.834 | 77% | 66.9% | 0% | 0% | 10.2% |
| Alltraits insect | spL | 0.446 | 0.570 | 0.322 | -0.181 | 1.073 | 61.4% | 0% | 14.2% | 0% | 47.3% |
| Alltraits insect | hybLS | 0.226 | 0.403 | 0.048 | -0.892 | 1.343 | 80.5% | 0% | 8.9% | 0% | 71.6% |
| Alltraits insect | hybSL | 0.151 | 0.362 | -0.061 | -0.957 | 1.259 | 79.5% | 0% | 23.3% | 0% | 56.2% |
| Nonnoveltraits insect | spL | 0.647 | 0.870 | 0.424 | -0.185 | 1.479 | 70.2% | 0% | 24.3% | 0% | 45.9% |
| Nonnoveltraits insect | hybLS | 0.317 | 0.543 | 0.091 | -0.488 | 1.122 | 60.6% | 0% | 22.1% | 0% | 38.5% |
| Nonnoveltraits insect | hybSL | 0.256 | 0.437 | 0.076 | -0.222 | 0.735 | 33.5% | 0% | 21% | 11% | 1.5% |
We statistically compared lnCVR from spSSM to the other crosses through the formal meta-regression by using rma.mv function of R package metafor.
Here we asked if lnCVR of hybSLV was smaller/larger than that of hybLSV. Smaller lnCVR of hybSLV indicates maternal inheritance in phenotypic variability
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- Novel.Nonnovel %>%
filter(data.type == tr)
} else {
dat <- Novel.Nonnovel %>%
filter(data.type == tr, insect == taxon)
}
#++++++++++++++++++++++++++++++++++++++++++++++#
# phylogenetic random regresson (ANOVA)
#++++++++++++++++++++++++++++++++++++++++++++++#
# # Regress by lnCVR of phenotypic difference between parentals
# phyl.random.spL <- rma.mv(
# yi = lnCVR.es, V = lnCVR.sv,
# data = dat, method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ relevel(cross, ref = "spL")
# )
# phyl.random.hybLS <- rma.mv(
# yi = lnCVR.es, V = lnCVR.sv,
# data = dat, method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ cross
# )
# # Save model ###
# saveRDS(
# phyl.random.spL,
# file = paste("../Analysis/Variation.compare.from.spL", tr, taxon, "obj", sep = ".")
# )
# saveRDS(
# phyl.random.hybLS,
# file = paste("../Analysis/Variation.compare.from.hybLS", tr, taxon, "obj", sep = ".")
# )
phyl.random.hybLS <- readRDS(
paste("../Analysis/Variation.compare.from.hybLS", tr, taxon, "obj", sep = ".")
)
assign(
paste("Reciprocal", tr, taxon, sep = "."),
phyl.random.hybLS %>%
get_reg() %>%
# Show dataset name
within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
# Rename fixed effects
within('Fixed effects' <- c("", "hybLS (intrcpt)", "hybSL", "spLL")) %>%
# Difference of each cross from midparent in %
mutate('Comparison with hybridLS' = c(
rep("", 2),
paste(
round(
(exp(phyl.random.hybLS$beta[1]+phyl.random.hybLS$beta[2]) - exp(phyl.random.hybLS$beta[1]))*100,
2),
"% larger"
),
""
)
)
)
}
}
bind_rows(
Reciprocal.Alltraits.alltaxon, Reciprocal.Nonnoveltraits.alltaxon,
Reciprocal.Alltraits.insect, Reciprocal.Nonnoveltraits.insect
) %>%
kable("html", digits = 3, caption = "Table S6. Results of meta-analysis investigating cross direction effect in phenotypic variabilities using full dataset or data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | Comparison with hybridLS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | 78.2% | 20.1% | 9.6% | 0% | 48.5% | |||||||
| hybLS (intrcpt) | 0.377 | 0.055 | 0.700 | 0.022 |
|
|||||||
| hybSL | -0.074 | -0.155 | 0.006 | 0.070 | -10.44 % larger | |||||||
| spLL | 0.190 | 0.112 | 0.268 | 0.000 |
|
|||||||
| Nonnoveltraits alltaxon | 73.8% | 10.2% | 23% | 0% | 40.6% | |||||||
| hybLS (intrcpt) | 0.451 | 0.136 | 0.766 | 0.005 |
|
|||||||
| hybSL | -0.002 | -0.118 | 0.114 | 0.973 | -0.31 % larger | |||||||
| spLL | 0.362 | 0.251 | 0.474 | 0.000 |
|
|||||||
| Alltraits insect | 72.1% | 0.8% | 14.2% | 0% | 57.1% | |||||||
| hybLS (intrcpt) | 0.258 | 0.095 | 0.420 | 0.002 |
|
|||||||
| hybSL | -0.100 | -0.183 | -0.016 | 0.019 |
|
-12.26 % larger | ||||||
| spLL | 0.178 | 0.098 | 0.258 | 0.000 |
|
|||||||
| Nonnoveltraits insect | 65.2% | 0% | 24.7% | 0% | 40.5% | |||||||
| hybLS (intrcpt) | 0.329 | 0.107 | 0.550 | 0.004 |
|
|||||||
| hybSL | -0.032 | -0.153 | 0.088 | 0.598 | -4.42 % larger | |||||||
| spLL | 0.354 | 0.240 | 0.468 | 0.000 |
|
dat.full <- read.csv("../data/mean.ES.Novelty.csv", head = TRUE)
## N of novel phenotype in each taxa and each direction ###
summary <- bind_rows(
# All observations
dat.full %>%
group_by(taxa) %>%
summarise(
'Species pair' = length(unique(species.pair)),
Observations = length(unique(ES.ID)),
Study = length(unique(Study.ID))
) %>%
mutate(direction = "All Observations")
,
# novel phenotype for any direction
dat.full %>%
filter(Novelty == "1") %>%
group_by(taxa) %>%
summarise(
'Species pair' = length(unique(species.pair)),
Observations = length(unique(ES.ID)),
Study = length(unique(Study.ID))
) %>%
mutate(direction = "Novel phenotypic means")
,
# novel phenotype for each direction
dat.full %>%
filter(Novelty == "1") %>%
group_by(taxa, direction) %>%
summarise(
'Species pair' = length(unique(species.pair)),
Observations = length(unique(ES.ID)),
Study = length(unique(Study.ID))
)
) %>%
# Assign 0 for non-novel phenotype
replace(., is.na(.), "0") %>%
# Make tidy data
gather(key = metrics, value = N, -c(taxa, direction)) %>%
# Order factors
mutate_at(vars("metrics", "direction"), as.factor) %>%
filter(metrics != "Study")
# Order taxon
summary$metrics <- ordered(summary$metrics, levels = c("Study", "Species pair", "Observations"))
# Order novel phenotype category
summary$direction <- ordered(summary$direction, levels = c("All Observations", "Novel phenotypic means", "+", "-"))
# Change names of TS category to more intuitive name
levels(summary$direction) <- c("All Observations", "Novel phenotypic means", "Exceed upper range", "Exceed lower range")
## Plot ###
plot <- ggplot(
# rename levels of factors to wrap label text
summary %>%
within(levels(direction) <- c("All Obser- vations", "Novel phenotypic means", "Exceed upper range", "Exceed lower range")) %>%
within(levels(metrics) <- c("Study", "Species pair", "Obser- vations")),
aes(x = "", y = N, fill = taxa)) +
geom_bar(width = 1, stat = "identity") +
scale_fill_brewer(palette = "Paired") +
facet_grid(
scale = "free", metrics ~ direction,
# strip text into two lines
labeller = label_wrap_gen(width = 10)
) +
xlab("") + ylab("") +
stackedbartheme
plot +
ggtitle("Taxonomic distribution of novel phenotypic means")
# ggsave(
# plot = plot,
# file = "../Analysis/mean.TS.frequency.svg", height = 3.7, width = 5.0
# )
## Total number of observations and percentage ###
summary %>%
group_by(direction, metrics) %>%
# Count number of observaitons and species pairs
summarise(sum(N)) %>%
spread(key = metrics, value = "sum(N)") %>%
as.data.frame() %>%
# Calculate percentage
mutate(
'Species pair %' = .[, "Species pair"]/.[1, "Species pair"]*100,
'Observaton %' = Observations/.[1, "Observations"]*100
) %>%
mutate_at(vars(contains("percent")), round, 2) %>%
kable("html", digits = 3, caption = "Summary of the novel phenotypic means frequency") %>%
kable_styling("striped", position = "left")
| direction | Species pair | Observations | Species pair % | Observaton % |
|---|---|---|---|---|
| All Observations | 34 | 332 | 100.000 | 100.000 |
| Novel phenotypic means | 22 | 143 | 64.706 | 43.072 |
| Exceed upper range | 15 | 71 | 44.118 | 21.386 |
| Exceed lower range | 11 | 74 | 32.353 | 22.289 |
We checked robustness of results by conducting identical analysis for insect subset data (removing vertebrate data).
Model, which was ran using MCMCglmm function, included following Random effects estimates:
Study.ID: Primary studies
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations
spL.name: Phylogeny of parental species (spLLM)
SE.units: Sampling variance of effect size
The moderators were categorised into 3 subsets: main effects (labelled as novelty in phenotypic means), interaction terms with compared parental species (spLL vs. spSS, labelled as exceed upper range; mother species vs. father, labelled as exceed mother).
# Correcting estimate of binomial regression
c2 <- (16 * sqrt(3)/(15 * pi))^2
#++++++++++++++++++++++++++++++++++#
# Phylogenetic tree for mcmcglmm
#++++++++++++++++++++++++++++++++++#
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.mean.tre") %>%
compute.brlen(method = "Grafen", power = 1)
# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)
# generating inverse phylogenetic matrix for MCMCglmm
phylo_branch$node.label <- NULL
phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv
# Caption
cap <- data.frame(
desc = c(
"Table S7. Fixed effects estimates of Bayesian logistic model investigating factors affecting novelty in phenotypic means. Data of all taxon was used",
"Fixed effects estimates: insect subset data"
)
) %>%
mutate_all(as.character)
row.names(cap) <- c("alltaxon", "insect")
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- dat.full
} else {
dat <- dat.full %>%
filter(insect == taxon)
}
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 17), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~
# parental*Hetero.sex +
# parental*direction +
# direction*Genet.divergence + direction*Pheno.divergence +
# direction*Hetero.sex +
# direction*trait.type +
# direction*Distribution + direction*Reciprocal,
# data = dat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~
# parental*Hetero.sex + parental*direction +
# direction*Genet.divergence + direction*Pheno.divergence +
# direction*Hetero.sex + direction*trait.type +
# direction*Distribution + direction*Reciprocal,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# family = "categorical",
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = dat,
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file = paste("../Analysis/Mean.novelty.allfactors", taxon, "obj", sep = "."))
#+++++++++++++++++++++++#
# Grouping variables
#+++++++++++++++++++++++#
# Load model
phylmix <- readRDS(paste("../Analysis/Mean.novelty.allfactors", taxon, "obj", sep = "."))
sum <- get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
## Grouping Factors ###
mutate(
Group =
ifelse(str_detect(Factors, "direction.:"),
"Exceed upper range",
ifelse(str_detect(Factors, "parentalMother:"),
"Exceed mother",
ifelse(Factors == "Hetero.sexMale:direction+",
"Exceed upper range",
"Increase novelty"
)
)
)
) %>%
mutate_at("Group", as.factor) %>%
within(Group <- ordered(Group, c("Increase novelty", "Exceed upper range", "Exceed mother"))) %>%
## Change factor names ###
within(
Factors <- str_remove_all(
Factors, "direction.:"
) %>%
str_remove_all(., "parentalMother:") %>%
str_remove_all(., ":direction.")
) %>%
within(
Factors <- factor(
Factors, ordered = TRUE,
levels = c("parentalMother", "direction+", "trait.typesound", "Hetero.sexMale", "ReciprocalViable", "DistributionOverlap", "Pheno.divergence", "Genet.divergence", "(Intercept)")
)
)
levels(sum$Factors) <- c("Exceed mother", "Exceed upper range", "Sound traits", "Male heterogametic", "Viable reciprocal hybrids", "Distribution overlap", "Phenotypic divergence", "Genetic divergence", "Intercept")
## Plot ###
metaplot <- ggplot(sum, aes(x = post.mean, y = Factors)) +
# Vertical line
geom_vline(
xintercept = 0, size = 0.2,
colour = "grey30", linetype = "dotted"
) +
# CI
geom_errorbarh(
aes(
xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI'],
colour=significance
),
height = .0001
) +
# Color of plots and errorbars
scale_colour_manual(values = c("grey60", "black")) +
geom_point(size = 1, aes(colour = significance)) +
scale_fill_manual(values = c("grey60", "black")) +
# Title
ggtitle(paste("Fixed effects estimates using", taxon, "data")) +
ylab("") + xlab("Estimate with 95% CI") +
# Combine different plots for main Factors and interactions
facet_grid(
Group~., scales = "free", space = "free",
labeller = label_wrap_gen(width = 20)
) +
# Themes
foresttheme
print(metaplot)
# ggsave(
# plot = metaplot,
# file = paste("../Analysis/mean.novelty.result", taxon, "svg", sep = "."),
# height = 4.7, width = 4.0
# )
#+++++++++++++++++++++++++#
# Fixed effects output
#+++++++++++++++++++++++++#
sum %>%
select(Group, Factors, Estimates, '95% credible interval', P, significance, Description) %>%
kable("html", caption = cap[taxon,]) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px") %>%
print()
#+++++++++++++++++++++++++++#
# Random effects output
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV/(1+c2))) %>%
kable("html", digits = 2,
caption = paste("Random effects estimates: ", taxon, "data", " ")
) %>%
kable_styling("striped", position = "left") %>%
print()
}
| Group | Factors | Estimates | 95% credible interval | P | significance | Description |
|---|---|---|---|---|---|---|
| Increase novelty | Intercept | -1.82 | -5.19 – 1.64 | 0.269 | \(\beta\) = -1.82, CI = -5.19 – 1.64, P = 0.269 | |
| Increase novelty | Exceed mother | -0.33 | -2.49 – 1.57 | 0.743 | \(\beta\) = -0.33, CI = -2.49 – 1.57, P = 0.743 | |
| Increase novelty | Male heterogametic | -0.46 | -2.87 – 2.16 | 0.717 | \(\beta\) = -0.46, CI = -2.87 – 2.16, P = 0.717 | |
| Increase novelty | Exceed upper range | 0.25 | -1.9 – 2.41 | 0.833 | \(\beta\) = 0.25, CI = -1.9 – 2.41, P = 0.833 | |
| Increase novelty | Genetic divergence | 0.26 | -0.41 – 0.9 | 0.420 | \(\beta\) = 0.26, CI = -0.41 – 0.9, P = 0.42 | |
| Increase novelty | Phenotypic divergence | -0.80 | -1.48 – -0.23 | 0.000 |
|
\(\beta\) = -0.8, CI = -1.48 – -0.23, P = 0 |
| Increase novelty | Sound traits | 0.94 | -1.14 – 2.94 | 0.357 | \(\beta\) = 0.94, CI = -1.14 – 2.94, P = 0.357 | |
| Increase novelty | Distribution overlap | -1.39 | -3.54 – 0.52 | 0.138 | \(\beta\) = -1.39, CI = -3.54 – 0.52, P = 0.138 | |
| Increase novelty | Viable reciprocal hybrids | -1.36 | -2.97 – 0.04 | 0.064 | \(\beta\) = -1.36, CI = -2.97 – 0.04, P = 0.064 | |
| Exceed mother | Male heterogametic | 1.68 | -0.38 – 3.63 | 0.104 | \(\beta\) = 1.68, CI = -0.38 – 3.63, P = 0.104 | |
| Exceed mother | Exceed upper range | -0.84 | -1.99 – 0.35 | 0.156 | \(\beta\) = -0.84, CI = -1.99 – 0.35, P = 0.156 | |
| Exceed upper range | Genetic divergence | -0.59 | -1.19 – 0.02 | 0.041 |
|
\(\beta\) = -0.59, CI = -1.19 – 0.02, P = 0.041 |
| Exceed upper range | Phenotypic divergence | 0.08 | -0.57 – 0.74 | 0.790 | \(\beta\) = 0.08, CI = -0.57 – 0.74, P = 0.79 | |
| Exceed upper range | Male heterogametic | 0.69 | -1.6 – 2.77 | 0.532 | \(\beta\) = 0.69, CI = -1.6 – 2.77, P = 0.532 | |
| Exceed upper range | Sound traits | -1.35 | -3.14 – 0.3 | 0.116 | \(\beta\) = -1.35, CI = -3.14 – 0.3, P = 0.116 | |
| Exceed upper range | Distribution overlap | -0.30 | -1.95 – 1.33 | 0.715 | \(\beta\) = -0.3, CI = -1.95 – 1.33, P = 0.715 | |
| Exceed upper range | Viable reciprocal hybrids | 2.51 | 1.01 – 4.01 | 0.001 |
|
\(\beta\) = 2.51, CI = 1.01 – 4.01, P = 0.001 |
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 1.37 | 0–6.03 |
| Cross.ID | 0.42 | 0–2.53 |
| spL.name | 4.16 | 0–26.95 |
| SE.units | 15.59 | 0.07–84.7 |
| units | 0.74 | 0.74–0.74 |
| Group | Factors | Estimates | 95% credible interval | P | significance | Description |
|---|---|---|---|---|---|---|
| Increase novelty | Intercept | -0.62 | -5.59 – 4.89 | 0.800 | \(\beta\) = -0.62, CI = -5.59 – 4.89, P = 0.8 | |
| Increase novelty | Exceed mother | -0.23 | -2.57 – 2.11 | 0.845 | \(\beta\) = -0.23, CI = -2.57 – 2.11, P = 0.845 | |
| Increase novelty | Male heterogametic | -0.01 | -3.11 – 3.38 | 0.996 | \(\beta\) = -0.01, CI = -3.11 – 3.38, P = 0.996 | |
| Increase novelty | Exceed upper range | 0.79 | -1.88 – 3.39 | 0.560 | \(\beta\) = 0.79, CI = -1.88 – 3.39, P = 0.56 | |
| Increase novelty | Genetic divergence | 0.06 | -0.94 – 0.92 | 0.851 | \(\beta\) = 0.06, CI = -0.94 – 0.92, P = 0.851 | |
| Increase novelty | Phenotypic divergence | -1.28 | -2.23 – -0.3 | 0.004 |
|
\(\beta\) = -1.28, CI = -2.23 – -0.3, P = 0.004 |
| Increase novelty | Sound traits | 0.39 | -2.55 – 3.3 | 0.773 | \(\beta\) = 0.39, CI = -2.55 – 3.3, P = 0.773 | |
| Increase novelty | Distribution overlap | -1.76 | -4.63 – 1.01 | 0.223 | \(\beta\) = -1.76, CI = -4.63 – 1.01, P = 0.223 | |
| Increase novelty | Viable reciprocal hybrids | -0.90 | -3.27 – 1.32 | 0.435 | \(\beta\) = -0.9, CI = -3.27 – 1.32, P = 0.435 | |
| Exceed mother | Male heterogametic | 0.76 | -1.55 – 3.24 | 0.531 | \(\beta\) = 0.76, CI = -1.55 – 3.24, P = 0.531 | |
| Exceed mother | Exceed upper range | -0.03 | -2.06 – 2.01 | 0.943 | \(\beta\) = -0.03, CI = -2.06 – 2.01, P = 0.943 | |
| Exceed upper range | Genetic divergence | -0.77 | -1.55 – -0.03 | 0.042 |
|
\(\beta\) = -0.77, CI = -1.55 – -0.03, P = 0.042 |
| Exceed upper range | Phenotypic divergence | -0.47 | -1.54 – 0.54 | 0.369 | \(\beta\) = -0.47, CI = -1.54 – 0.54, P = 0.369 | |
| Exceed upper range | Male heterogametic | 1.88 | -0.7 – 4.68 | 0.160 | \(\beta\) = 1.88, CI = -0.7 – 4.68, P = 0.16 | |
| Exceed upper range | Sound traits | -0.97 | -3.41 – 1.38 | 0.416 | \(\beta\) = -0.97, CI = -3.41 – 1.38, P = 0.416 | |
| Exceed upper range | Distribution overlap | -0.89 | -3.21 – 1.31 | 0.433 | \(\beta\) = -0.89, CI = -3.21 – 1.31, P = 0.433 | |
| Exceed upper range | Viable reciprocal hybrids | 0.94 | -1.35 – 3.2 | 0.403 | \(\beta\) = 0.94, CI = -1.35 – 3.2, P = 0.403 |
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 5.97 | 0–31.8 |
| Cross.ID | 2.84 | 0–16.35 |
| spL.name | 30.36 | 0.18–119.64 |
| SE.units | 112.29 | 0.49–336.88 |
| units | 0.74 | 0.74–0.74 |
Reciprocal hybrids) become no longer significant in insect subset data, effects are qualitatively the same in sign.
To visually interpret the impacts of significant moderators, we additionally conducted simpler models for each significant factor. As moderators, we included the focal factors, the compared parental species – spLL or spSS, and the interaction between them. Response variables and random effects of simper models were identical to full models.
# +++++++++++++++++++++++++++++++++++++++++++#
# To set raw metadata in X axis, reload raw metadata and combine to novelty dataset
# +++++++++++++++++++++++++++++++++++++++++++#
rawdat <- dat.full %>%
select(-Pheno.divergence, -Genet.divergence) %>%
# lnRR between parental species as phenotypic distance
mutate(Pheno.divergence = log(Mn.spL/Mn.spS)) %>%
left_join(
.,
read.xlsx("../data/original.data.xlsx", sheet = "Species.level.moderators") %>%
select(Cross.ID, Genet.divergence)
) %>%
# Natural log of genetic distance
mutate_at("Genet.divergence", log) %>%
mutate_at("direction", as.factor)
# +++++++++++++++++++++++++++++++++++++++++++#
# Regression: Genetic distance
# +++++++++++++++++++++++++++++++++++++++++++#
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~ direction:Genet.divergence + Genet.divergence,
# data = rawdat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~ direction:Genet.divergence + Genet.divergence,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# family = "categorical",
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = rawdat,
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Mean.novelty.Genet.divergence.obj")
# +++++++++++++++++++++++++++++++++++++++++++#
# Regression: Phenotypic divergence
# +++++++++++++++++++++++++++++++++++++++++++#
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~ direction:Pheno.divergence + Pheno.divergence,
# data = rawdat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~ direction:Pheno.divergence + Pheno.divergence,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# family = "categorical",
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = rawdat,
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Mean.novelty.Pheno.divergence.obj")
#++++++++++++++++++++++++++++++++++++++++++++++++++#
# Plot
#++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("Genet.divergence", "Pheno.divergence")) {
## Load model ###
phylmix <- readRDS(
paste("../Analysis/Mean.novelty",i, "obj", sep = ".")
)
reg <- summary(phylmix$Sol)$statistics %>%
as.data.frame()
## Plot ###
plot <- ggplot(rawdat, aes(x = rawdat[,i], y = rawdat[, "Novelty"])) +
geom_jitter(
height = 0.1, width = 0, aes(color = direction), alpha = 0.3, size = 0.2
) +
# novelty for larger mean
stat_function(
aes(color = "+"), size = 0.8,
fun = function(x)
1/(1 + exp(-(reg[1,1] + x*(reg[2,1]+reg[3,1]))))
) +
# novelty for smaller mean
stat_function(
aes(color = "-"), size = 0.8,
fun = function(x)
1/(1 + exp(-(reg[1,1] + x*reg[2,1])))
) +
ylab("Novelty in phenotypic means") +
xlab(paste(i, "(log)")) +
scale_fill_brewer(palette = "Set2") +
scale_y_continuous(breaks=seq(0,1)) +
regressiontheme
print(plot)
# ggsave(
# plot = plot + theme(axis.title = element_blank()),
# file = paste("../Analysis/mean.TS.continuous", i, "svg", sep = "."),
# height = 1.6, width = 1.7
# )
}
for (i in c("Reciprocal", "trait.type")) {
# Make new categorical data combining direction of TS and target categorical factor
dat %<>%
mutate(interaction = str_c(direction, .[, i], sep = "_"))
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 4), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~ interaction, # + trait.direction
# data = dat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# # Replicating same G for the number of random effects (here, 4)
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
#
# phylmix <-MCMCglmm(
# fixed = Novelty ~ interaction -1, # + trait.direction
# family = "categorical",
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = dat
# )
# # Correct model result
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(
# phylmix,
# file =
# paste("../Analysis/Mean.novelty", i, "alltaxon.obj", sep = ".")
# )
#++++++++++++++++++++++++++++++++++++++#
# ing interaction terms
#++++++++++++++++++++++++++++++++++++++#
phylmix <- readRDS(
paste("../Analysis/Mean.novelty", i, "alltaxon.obj", sep = ".")
)
sum <- summary(phylmix)$solutions %>%
as.data.frame() %>%
rownames_to_column(var = "factors") %>%
mutate(
# Impact on exceeding upper or lower range
direction =
ifelse(str_detect(factors, "interaction\\+"),
"Exceed upper range",
"Exceed lower range"
) %>%
as.factor
) %>%
within(direction <- ordered(direction, c("Exceed upper range", "Exceed lower range"))) %>%
## Change factor names ###
within(
factors <- str_remove_all(factors, "interaction._")
)
#++++++++++++++++++++++++++++++++++++++#
# Plot
#++++++++++++++++++++++++++++++++++++++#
categoricalplot <- ggplot(sum, aes(x = post.mean, y = factors)) +
# Vertical line
geom_vline(
xintercept = 0, size = 0.3,
colour = "grey30", linetype = "dotted"
) +
# CI
geom_errorbarh(
aes(xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI']),
height = .0001
) +
geom_point(size = 1.3, shape = 17) +
# Title
ylab("") + xlab("Effect on novelty in phenotypic means") +
ggtitle(i) +
# Combine different plots for main factors and interactions
facet_grid(
direction ~., scales = "free", drop = TRUE,
labeller = label_wrap_gen(width = 11)
) +
foresttheme
print(categoricalplot)
# ggsave(
# plot = categoricalplot + theme(axis.text.y = element_blank()),
# file = paste("../Analysis/Mean.TS.categorical", i,taxon, "svg", sep = "."),
# height = 1.8, width = 2.5
# )
}
dat.full <- read.csv("../data/variation.ES.Novelty.csv", head = TRUE)
## N of TS in each taxa and each direction ###
summary <- bind_rows(
# All observations
dat.full %>%
group_by(taxa) %>%
summarise(
'Species pair' = length(unique(species.pair)),
Observations = length(unique(ES.ID)),
Study = length(unique(Study.ID))
) %>%
mutate(direction = "All Observations")
,
# TS for any direction
dat.full %>%
filter(Novelty == "1") %>%
group_by(taxa) %>%
summarise(
'Species pair' = length(unique(species.pair)),
Observations = length(unique(ES.ID)),
Study = length(unique(Study.ID))
) %>%
mutate(direction = "Increase novelty")
,
# TS for each direction
dat.full %>%
filter(Novelty == "1") %>%
group_by(taxa, direction) %>%
summarise(
'Species pair' = length(unique(species.pair)),
Observations = length(unique(ES.ID)),
Study = length(unique(Study.ID))
)
) %>%
# Assign 0 for no observation of TS
replace(., is.na(.), "0") %>%
# Make tidy data
gather(key = metrics, value = N, -c(taxa, direction)) %>%
# Order factors
mutate_at(vars("metrics", "direction"), as.factor) %>%
filter(metrics != "Study")
# Order taxon
summary$metrics <- ordered(summary$metrics, levels = c("Study", "Species pair", "Observations"))
# Order TS category
summary$direction <- ordered(summary$direction, levels = c("All Observations", "Increase novelty", "+", "-"))
# Change names of TS category to more intuitive name
levels(summary$direction) <- c("All Observations", "Increase novelty", "Exceed upper range", "Exceed lower range")
## Plot ###
plot <- ggplot(
# rename levels of factors to wrap label text
summary %>%
within(levels(direction) <- c("All Obser- vations", "Novel phenotypic variabilities", "Exceed upper range", "Exceed lower range")) %>%
within(levels(metrics) <- c("Study", "Species pair", "Obser- vations")),
aes(x = "", y = N, fill = taxa)) +
geom_bar(width = 1, stat = "identity") +
scale_fill_brewer(palette = "Paired") +
facet_grid(
scale = "free", metrics ~ direction,
# strip text into two lines
labeller = label_wrap_gen(width = 10)
) +
xlab("") + ylab("") +
stackedbartheme
plot +
ggtitle("Taxonomic distribution of novel phenotypic variabilities")
# ggsave(
# plot = plot,
# file = "../Analysis/Variation.TS.frequency.svg", height = 3.7, width = 5.0
# )
## Total number of observations and percentage ###
summary %>%
group_by(direction, metrics) %>%
# Count number of observaitons and species pairs
summarise(sum(N)) %>%
spread(key = metrics, value = "sum(N)") %>%
as.data.frame() %>%
# Calculate percentage
mutate(
'Species pair %' = .[, "Species pair"]/.[1, "Species pair"]*100,
'Observaton %' = Observations/.[1, "Observations"]*100
) %>%
mutate_at(vars(contains("percent")), round, 2) %>%
kable("html", digits = 3, caption = "Summary of the novel phenotypic variabilities frequency") %>%
kable_styling("striped", position = "left")
| direction | Species pair | Observations | Species pair % | Observaton % |
|---|---|---|---|---|
| All Observations | 33 | 331 | 100.000 | 100.000 |
| Increase novelty | 31 | 248 | 93.939 | 74.924 |
| Exceed upper range | 18 | 82 | 54.545 | 24.773 |
| Exceed lower range | 26 | 170 | 78.788 | 51.360 |
We checked robustness of results by conducting identical analysis for insect subset data (removing vertebrate data).
Model, which was ran using MCMCglmm function, included following Random effects estimates:
Study.ID: Primary studies
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations
spL.name: Phylogeny of parental species (spLLV)
SE.units: Sampling variance of effect size
The moderators were categorised into 3 subsets: main effects (labelled as novelty in phenotypic variabilities), interaction terms with compared parental species (spLL vs. spSS, labelled as exceed upper range; mother species vs. father, labelled as exceed mother).
#++++++++++++++++++++++++++++++++++#
# Phylogenetic tree for mcmcglmm
#++++++++++++++++++++++++++++++++++#
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.variation.tre") %>%
compute.brlen(method = "Grafen", power = 1)
# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)
# generating inverse phylogenetic matrix for MCMCglmm
phylo_branch$node.label <- NULL
phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv
#+++++++++++++++++++++++++++++++++++++#
# Phylogenetic comparative analysis
#+++++++++++++++++++++++++++++++++++++#
# Caption
cap <- data.frame(
desc = c(
"Table S8. Fixed effects estimates of Bayesian logistic model investigating factors affecting novelty in phenotypic variabilities. Data of all taxon was used",
"Fixed effects estimates: insect subset data"
)
) %>%
mutate_all(as.character)
row.names(cap) <- c("alltaxon", "insect")
for (taxon in c("alltaxon", "insect")) {
if (taxon == "alltaxon") {
dat <- dat.full
} else {
dat <- dat.full %>%
filter(insect == taxon)
}
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 17), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~
# parental*Hetero.sex + parental*direction +
# direction*Genet.divergence + direction*Pheno.divergence +
# direction*Hetero.sex + direction*trait.type +
# direction*Distribution + direction*Reciprocal,
# data = dat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# # Replicating same G for the number of random effects (here, 4)
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~
# parental*Hetero.sex + parental*direction +
# direction*Genet.divergence + direction*Pheno.divergence +
# direction*Hetero.sex + direction*trait.type +
# direction*Distribution + direction*Reciprocal,
# family = "categorical",
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = dat,
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
# ## Correcting estimate ###
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(
# phylmix,
# file = paste("../Analysis/Variation.novelty.allfactors", taxon, "obj", sep = ".")
# )
## Load model ###
phylmix <- readRDS(file = paste("../Analysis/Variation.novelty.allfactors", taxon, "obj", sep = "."))
sum <- get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
## ing Factors ###
mutate(
Group =
ifelse(str_detect(Factors, "direction.:"),
"Exceed upper range",
ifelse(str_detect(Factors, "parentalMother:"),
"Exceed mother",
ifelse(Factors == "Hetero.sexMale:direction+",
"Exceed upper range",
"Increase novelty"
)
)
)
) %>%
mutate_at("Group", as.factor) %>%
within(Group <- ordered(Group, c("Increase novelty", "Exceed upper range", "Exceed mother"))) %>%
## Change factor names ###
within(
Factors <- str_remove_all(
Factors, "direction.:"
) %>%
str_remove_all(., "parentalMother:") %>%
str_remove_all(., ":direction.")
) %>%
within(
Factors <- factor(
Factors, ordered = TRUE,
levels = c("parentalMother", "direction+", "trait.typesound", "Hetero.sexMale", "ReciprocalViable", "DistributionOverlap", "Pheno.divergence", "Genet.divergence", "(Intercept)")
)
)
levels(sum$Factors) <- c("Exceed mother", "Exceed upper range", "Sound traits", "Male heterogametic", "Viable reciprocal hybrids", "Distribution overlap", "Phenotypic divergence", "Genetic divergence", "Intercept")
## Plot ###
metaplot <- ggplot(sum, aes(x = post.mean, y = Factors)) +
# Vertical line
geom_vline(
xintercept = 0, size = 0.2,
colour = "grey30", linetype = "dotted"
) +
# CI
geom_errorbarh(
aes(
xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI'],
colour=significance
),
height = .0001
) +
# Color of plots and errorbars
scale_colour_manual(values = c("grey60", "black")) +
geom_point(size = 1, aes(colour = significance)) +
scale_fill_manual(values = c("grey60", "black")) +
# Title
ggtitle(paste("Fixed effects estimates using", taxon, "data")) +
ylab("") + xlab("Estimate with 95% CI") +
# Combine different plots for main Factors and interactions
facet_grid(
Group~., scales = "free", space = "free",
labeller = label_wrap_gen(width = 20)
) +
# Themes
foresttheme
print(metaplot)
# ggsave(
# plot = metaplot,
# file = paste("../Analysis/variation.TS.result", taxon, "svg", sep = "."),
# height = 4.7, width = 4.0
# )
#+++++++++++++++++++++++++#
# Fixed effects output
#+++++++++++++++++++++++++#
sum %>%
select(Group, Factors, Estimates, '95% credible interval', P, significance, Description) %>%
kable("html", caption = cap[taxon,]) %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px") %>%
print()
#+++++++++++++++++++++++++++#
# Random effects output
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV/(1+c2))) %>%
kable("html", digits = 2,
caption = paste("Random effects estimates: ", taxon, "data", " ")
) %>%
kable_styling("striped", position = "left") %>%
print()
}
| Group | Factors | Estimates | 95% credible interval | P | significance | Description |
|---|---|---|---|---|---|---|
| Increase novelty | Intercept | -0.41 | -3.57 – 3.2 | 0.769 | \(\beta\) = -0.41, CI = -3.57 – 3.2, P = 0.769 | |
| Increase novelty | Exceed mother | 0.54 | -1.43 – 2.43 | 0.588 | \(\beta\) = 0.54, CI = -1.43 – 2.43, P = 0.588 | |
| Increase novelty | Male heterogametic | -0.06 | -2.68 – 2.33 | 0.961 | \(\beta\) = -0.06, CI = -2.68 – 2.33, P = 0.961 | |
| Increase novelty | Exceed upper range | -1.36 | -3.52 – 0.78 | 0.210 | \(\beta\) = -1.36, CI = -3.52 – 0.78, P = 0.21 | |
| Increase novelty | Genetic divergence | -0.47 | -1.23 – 0.26 | 0.220 | \(\beta\) = -0.47, CI = -1.23 – 0.26, P = 0.22 | |
| Increase novelty | Phenotypic divergence | -0.39 | -1.02 – 0.2 | 0.208 | \(\beta\) = -0.39, CI = -1.02 – 0.2, P = 0.208 | |
| Increase novelty | Sound traits | 1.68 | -0.31 – 3.83 | 0.109 | \(\beta\) = 1.68, CI = -0.31 – 3.83, P = 0.109 | |
| Increase novelty | Distribution overlap | -0.69 | -2.55 – 1.26 | 0.464 | \(\beta\) = -0.69, CI = -2.55 – 1.26, P = 0.464 | |
| Increase novelty | Viable reciprocal hybrids | -1.07 | -2.72 – 0.62 | 0.203 | \(\beta\) = -1.07, CI = -2.72 – 0.62, P = 0.203 | |
| Exceed mother | Male heterogametic | -1.44 | -3.45 – 0.43 | 0.142 | \(\beta\) = -1.44, CI = -3.45 – 0.43, P = 0.142 | |
| Exceed mother | Exceed upper range | -0.22 | -1.7 – 1.33 | 0.790 | \(\beta\) = -0.22, CI = -1.7 – 1.33, P = 0.79 | |
| Exceed upper range | Genetic divergence | 1.25 | 0.47 – 2 | 0.000 |
|
\(\beta\) = 1.25, CI = 0.47 – 2, P = 0 |
| Exceed upper range | Phenotypic divergence | 0.83 | 0.01 – 1.61 | 0.036 |
|
\(\beta\) = 0.83, CI = 0.01 – 1.61, P = 0.036 |
| Exceed upper range | Male heterogametic | 0.47 | -1.84 – 2.7 | 0.678 | \(\beta\) = 0.47, CI = -1.84 – 2.7, P = 0.678 | |
| Exceed upper range | Sound traits | -3.29 | -5.21 – -1.38 | 0.001 |
|
\(\beta\) = -3.29, CI = -5.21 – -1.38, P = 0.001 |
| Exceed upper range | Distribution overlap | 1.32 | -0.53 – 3.08 | 0.151 | \(\beta\) = 1.32, CI = -0.53 – 3.08, P = 0.151 | |
| Exceed upper range | Viable reciprocal hybrids | 1.19 | -0.55 – 2.98 | 0.177 | \(\beta\) = 1.19, CI = -0.55 – 2.98, P = 0.177 |
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 2.07 | 0.02–7.41 |
| Cross.ID | 0.53 | 0–2.78 |
| spL.name | 2.54 | 0–13.9 |
| SE.units | 92.08 | 37.22–172.67 |
| units | 0.74 | 0.74–0.74 |
| Group | Factors | Estimates | 95% credible interval | P | significance | Description |
|---|---|---|---|---|---|---|
| Increase novelty | Intercept | -0.22 | -5.65 – 5.45 | 0.912 | \(\beta\) = -0.22, CI = -5.65 – 5.45, P = 0.912 | |
| Increase novelty | Exceed mother | 0.62 | -1.83 – 3 | 0.627 | \(\beta\) = 0.62, CI = -1.83 – 3, P = 0.627 | |
| Increase novelty | Male heterogametic | 0.04 | -3.13 – 3.38 | 0.965 | \(\beta\) = 0.04, CI = -3.13 – 3.38, P = 0.965 | |
| Increase novelty | Exceed upper range | -1.14 | -3.82 – 1.56 | 0.403 | \(\beta\) = -1.14, CI = -3.82 – 1.56, P = 0.403 | |
| Increase novelty | Genetic divergence | -0.14 | -1.09 – 0.93 | 0.777 | \(\beta\) = -0.14, CI = -1.09 – 0.93, P = 0.777 | |
| Increase novelty | Phenotypic divergence | -0.41 | -1.39 – 0.57 | 0.408 | \(\beta\) = -0.41, CI = -1.39 – 0.57, P = 0.408 | |
| Increase novelty | Sound traits | 0.78 | -2.25 – 3.69 | 0.586 | \(\beta\) = 0.78, CI = -2.25 – 3.69, P = 0.586 | |
| Increase novelty | Distribution overlap | -0.57 | -3.54 – 2.24 | 0.695 | \(\beta\) = -0.57, CI = -3.54 – 2.24, P = 0.695 | |
| Increase novelty | Viable reciprocal hybrids | -0.78 | -3.2 – 1.82 | 0.527 | \(\beta\) = -0.78, CI = -3.2 – 1.82, P = 0.527 | |
| Exceed mother | Male heterogametic | -0.55 | -3.03 – 1.94 | 0.677 | \(\beta\) = -0.55, CI = -3.03 – 1.94, P = 0.677 | |
| Exceed mother | Exceed upper range | 0.25 | -2.25 – 2.62 | 0.842 | \(\beta\) = 0.25, CI = -2.25 – 2.62, P = 0.842 | |
| Exceed upper range | Genetic divergence | 0.83 | -0.1 – 1.78 | 0.085 | \(\beta\) = 0.83, CI = -0.1 – 1.78, P = 0.085 | |
| Exceed upper range | Phenotypic divergence | 1.04 | -0.1 – 2.15 | 0.067 | \(\beta\) = 1.04, CI = -0.1 – 2.15, P = 0.067 | |
| Exceed upper range | Male heterogametic | -0.21 | -3.07 – 2.59 | 0.896 | \(\beta\) = -0.21, CI = -3.07 – 2.59, P = 0.896 | |
| Exceed upper range | Sound traits | -2.45 | -4.95 – 0.41 | 0.079 | \(\beta\) = -2.45, CI = -4.95 – 0.41, P = 0.079 | |
| Exceed upper range | Distribution overlap | 0.79 | -1.78 – 3.36 | 0.544 | \(\beta\) = 0.79, CI = -1.78 – 3.36, P = 0.544 | |
| Exceed upper range | Viable reciprocal hybrids | 0.03 | -2.76 – 2.69 | 0.975 | \(\beta\) = 0.03, CI = -2.76 – 2.69, P = 0.975 |
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 13.88 | 0.36–51.78 |
| Cross.ID | 1.68 | 0–8.89 |
| spL.name | 18.74 | 0.08–74.21 |
| SE.units | 565.34 | 123.22–1224.95 |
| units | 0.74 | 0.74–0.74 |
insect subset data, effects are qualitatively the same in sign.
To visually interpret the impacts of significant moderators, we additionally conducted simpler models for each significant factor. As moderators, we included the focal factors, the compared parental species – spLL or spSS, and the interaction between them. Response variables and random effects of simper models were identical to full models.
# +++++++++++++++++++++++++++++++++++++++++++#
# To set raw metadata in X axis, reload raw metadata and combine to transgression dataset
# +++++++++++++++++++++++++++++++++++++++++++#
rawdat <- dat.full %>%
select(-Pheno.divergence, -Genet.divergence) %>%
# lnRR between parental species as phenotypic distance
mutate(Pheno.divergence = abs(log(Mn.spL/Mn.spS))) %>%
left_join(
.,
read.xlsx("../data/original.data.xlsx", sheet = "Species.level.moderators") %>%
select(Cross.ID, Genet.divergence)
) %>%
# Natural log of genetic distance
mutate_at("Genet.divergence", log)
# # +++++++++++++++++++++++++++++++++++++++++++#
# # Regression: Genetic distance
# # +++++++++++++++++++++++++++++++++++++++++++#
#
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~ direction:Genet.divergence + Genet.divergence,
# data = rawdat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~ direction:Genet.divergence + Genet.divergence,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# family = "categorical",
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = rawdat,
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Variation.novelty.Genet.divergence.obj")
#
# # +++++++++++++++++++++++++++++++++++++++++++#
# # Regression: Phenotypic divergence
# # +++++++++++++++++++++++++++++++++++++++++++#
#
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~ direction:Pheno.divergence + Pheno.divergence,
# data = rawdat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~ direction:Pheno.divergence + Pheno.divergence,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# family = "categorical",
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = rawdat,
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Variation.novelty.Pheno.divergence.obj")
#++++++++++++++++++++++++++++++++++++++++++++++++++#
# Plot
#++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("Genet.divergence", "Pheno.divergence")) {
## Load model ###
phylmix <- readRDS(
paste("../Analysis/Variation.novelty",i, "obj", sep = ".")
)
reg <- summary(phylmix$Sol)$statistics %>%
as.data.frame()
## Plot ###
plot <- ggplot(rawdat, aes(x = rawdat[,i], y = rawdat[, "Novelty"])) +
geom_jitter(
height = 0.1, width = 0, aes(color = direction), alpha = 0.3, size = 0.2
) +
# Larger variability
stat_function(
aes(color = "+"), size = 0.8,
fun = function(x)
1/(1 + exp(-(reg[1,1] + x*(reg[2,1]+reg[3,1]))))
) +
# Smaller variability
stat_function(
aes(color = "-"), size = 0.8,
fun = function(x)
1/(1 + exp(-(reg[1,1] + x*reg[2,1])))
) +
ylab("Novel phenotypic variabilities") +
xlab(paste(i, "(log)")) +
scale_fill_brewer(palette = "Set2") +
scale_y_continuous(breaks=seq(0,1)) +
regressiontheme
print(plot)
# ggsave(
# plot = plot,
# file = paste("../Analysis/Variation.TS.continuous", i, "svg", sep = "."),
# height = 1.6, width = 1.7
# )
}
for (i in c("Reciprocal", "trait.type")) {
# Make new categorical data combining direction of TS and target categorical factor
dat %<>%
mutate(interaction = str_c(direction, .[, i], sep = "_"))
# ## Setting prior to logistic regression ###
# prior <- list(
# B=list(
# mu = rep(0, 4), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
# V = gelman.prior(~ interaction,
# data = dat, # formula and data
# scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
# R=list(V=1,fix=1),
# # Replicating same G for the number of random effects (here, 4)
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
# ## Run model ###
# phylmix <-MCMCglmm(
# fixed = Novelty ~ interaction -1, # + trait.direction
# family = "categorical",
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
# verbose = FALSE,
# ginverse = list(spL.name = phylo_MCMC),
# prior = prior,
# data = dat
# )
# # Correcting estimate
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(
# phylmix,
# file =
# paste("../Analysis/Variation.novelty", i, "alltaxon.obj", sep = ".")
# )
#++++++++++++++++++++++++++++++++++++++#
# Grouping interaction terms
#++++++++++++++++++++++++++++++++++++++#
phylmix <- readRDS(
paste("../Analysis/Variation.novelty", i, "alltaxon.obj", sep = ".")
)
sum <- summary(phylmix)$solutions %>%
as.data.frame() %>%
rownames_to_column(var = "factors") %>%
mutate(
# Impact on exceeding upper or lower range
direction =
ifelse(str_detect(factors, "interaction\\+"),
"Exceed upper range",
"Exceed lower range"
) %>%
as.factor
) %>%
within(direction <- ordered(direction, c("Exceed upper range", "Exceed lower range"))) %>%
## Change factor names ###
within(
factors <- str_remove_all(factors, "interaction._")
)
#++++++++++++++++++++++++++++++++++++++#
# Plot
#++++++++++++++++++++++++++++++++++++++#
categoricalplot <- ggplot(sum, aes(x = post.mean, y = factors)) +
# Vertical line
geom_vline(
xintercept = 0, size = 0.3,
colour = "grey30", linetype = "dotted"
) +
# CI
geom_errorbarh(
aes(xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI']),
height = .0001
) +
geom_point(size = 1.3, shape = 17) +
# Title
ylab("") + xlab("Effect on novelty in phenotypic means") +
ggtitle(i) +
# Combine different plots for main factors and interactions
facet_grid(
direction ~., scales = "free", drop = TRUE,
labeller = label_wrap_gen(width = 11)
) +
foresttheme
print(categoricalplot)
# ggsave(
# plot = categoricalplot + theme(axis.text.y = element_blank()),
# file =
# paste("../Analysis/Variation.novelty.categorical", i,taxon, "svg", sep = "."),
# height = 1.8, width = 2.5
# )
}
Relative variability indicates F1 hybrids’ phenotypic variability size (CV) compared to those of parents.
Smaller: smaller variability than of both parents
Intermediate: intermediate between parents’ variability
Larger: larger variability than of both parents
dat <- read.csv("../data/dat.novelty.csv", head = TRUE) %>%
mutate_at(vars("mean.novelty", "relative.var"), as.factor) %>%
within(
relative.var <-
ordered(relative.var, levels = c("Smaller", "Intermediate", "Larger")),
mean.novelty <-
ordered(mean.novelty, levels = c("Nonnovel", "Novel"))
)
dat.relat.var <- dat %>%
group_by(mean.novelty, relative.var) %>%
summarise('N (Trait observation)' = length(ES.ID)) %>%
spread(key = mean.novelty, value = 'N (Trait observation)') %>%
as.data.frame() %>%
mutate(
"Nonnovel phenotype (% trait obs)" =
Nonnovel/sum(Nonnovel)*100,
"Novel phenotype (% trait obs)" =
Novel/sum(Novel)*100,
Trait.count = Nonnovel + Novel # N of traits at each variability levels
) %>%
rename('Relative.variability' = 'relative.var')
ggplot(
dat.relat.var %>%
dplyr::select(-contains("trait obs"), -Trait.count) %>%
pivot_longer(
col = -Relative.variability,
names_to = "Phenotypic.novelty",
values_to = "Count"
) %>%
left_join(dat.relat.var) %>%
as.data.frame %>%
mutate_at("Phenotypic.novelty", as.factor),
aes(
y = Count,
x = Trait.count/2, # ensure each pie chart is "left-aligned"
width = Trait.count, # sample size (N of traits at each variability levels)
fill = Phenotypic.novelty
)
) +
geom_bar(position="fill", stat = "identity") +
facet_grid(~ Relative.variability) +
# Pie chart
coord_polar("y") +
ggtitle("Novel and non-novel phenotypes occupancies at
each relative variability levels") +
xlab("Trait counts") + ylab("Relative variability levels") +
theme(axis.text.x = element_blank())
dat.relat.var %>%
rename(
'Nonnovel.phenotype (N)' = 'Nonnovel',
'Novel.phenotype (N)' = 'Novel'
) %>%
kable("html", digits = 1, caption = "Dataset summary") %>%
kable_styling("striped", position = "left")
| Relative.variability | Nonnovel.phenotype (N) | Novel.phenotype (N) | Nonnovel phenotype (% trait obs) | Novel phenotype (% trait obs) | Trait.count |
|---|---|---|---|---|---|
| Smaller | 146 | 74 | 49.2 | 40.4 | 220 |
| Intermediate | 96 | 54 | 32.3 | 29.5 | 150 |
| Larger | 55 | 55 | 18.5 | 30.1 | 110 |
#++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Read create tree based on Open Tree of Life taxonomy
#++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
# names = levels(dat$sp1.name) %>%
# str_replace_all("_", " "),
# context_name = "Animals"
# )
#
# # Create tree
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
# strip_ott_ids # remove OTT IDs from tip labels
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
# print("Randomly solved phylogeny")
#
# # Indicate mismatch between tip labels & dataset species names
# setdiff(levels(as.factor(bin.tree$tip.label)), levels(dat$sp1.name))
# setdiff(levels(dat$sp1.name), levels(as.factor(bin.tree$tip.label)))
# # Fix names of tip labels
# bin.tree$tip.label %<>% str_replace_all("Dryophytes", "Hyla")
#
# write.tree(bin.tree, file= "../data/phylo.novelty.tre")
#++++++++++++++++++++++++++++++++++#
# Phylogenetic tree for mcmcglmm
#++++++++++++++++++++++++++++++++++#
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.novelty.tre") %>%
compute.brlen(method = "Grafen", power = 1)
plot.phylo(phylo_branch, cex = 0.7)
Figure S3. Phylogenetic tree used, which was based on tree of the parental species within a cross which name comes earlier in alphabetical order
# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)
# generating inverse phylogenetic matrix for MCMCglmm
phylo_branch$node.label <- NULL
phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv
Here we ask if phenotypic novelty associate with relative variability levels. We conducted ordinal regression, employing categorical ordered variability levels (smaller < intermediate < larger, see above) as response variable, and categorical trait mean novelty levels (fixed effect mean.novelty with 2 levels: Novel vs Nonnovel) as moderator variable, using MCMCglmm function. The model included following Random effects estimates:
Study.ID: Primary studies
sp1.name: Phylogeny of parental species
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations
cross: Crossing direction
SE.units: Sampling variance of effect size
# prior1<-list(
# B=list(
# mu=c(0,0),
# V=gelman.prior(~mean.novelty, data=select(dat, -trait), scale=sqrt(1+1))
# ),
# R=list(V=1,fix=1),
# G = list(
# G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
# G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
# )
# )
#
# phylmix <-MCMCglmm(
# fixed = relative.var ~ mean.novelty,
# family = "ordinal",
# random = ~ Study.ID + Cross.ID + sp1.name + cross + ES.ID,
# verbose = FALSE,
# prior = prior1,
# ginverse = list(sp1.name = phylo_MCMC),
# data = select(dat, -trait),
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000 # Increase the number of burnin, default is 3000
# )
#
# ## Save model ###
# saveRDS(phylmix, file = "../Analysis/novelty.mean.var.obj")
## Load model ###
phylmix <- readRDS("../Analysis/novelty.mean.var.obj")
#+++++++++++++++++++++++++++#
# Fixed effects output
#+++++++++++++++++++++++++++#
get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
dplyr::select("Factors", "Estimates", "95% credible interval", "P", "Description") %>%
kable("html", digits = 3, caption = "Fixed effects estimates") %>%
kable_styling("striped", position = "left")
| Factors | Estimates | 95% credible interval | P | Description |
|---|---|---|---|---|
| (Intercept) | 0.23 | -1.72 – 1.94 | 0.678 | \(\beta\) = 0.23, CI = -1.72 – 1.94, P = 0.678 |
| mean.noveltyNovel | 1.01 | 0.45 – 1.62 | 0.000 | \(\beta\) = 1.01, CI = 0.45 – 1.62, P = 0 |
#+++++++++++++++++++++++++++#
# Random effects output
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV/(1+c2))) %>%
kable("html", digits = 2, caption = "Random effects estimates") %>%
kable_styling("striped", position = "left")
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 1.27 | 0.14–3.61 |
| Cross.ID | 0.23 | 0–1.12 |
| sp1.name | 0.52 | 0–3.04 |
| cross | 18.92 | 0–138.18 |
| ES.ID | 3.21 | 1.34–5.84 |
| units | 0.74 | 0.74–0.74 |
The phenotypic novelty (mean.noveltyNovel) influenced positively. This indicates that, compared to non-novel phenotypes, novel phenotypes have greater chances to be more variable than parents’ phenotypes.
F1 hybrids can be seen as “a mosaic of both parental and intermediate morphological characters rather than just intermediate ones” Rieseberg and Ellstrand 1993. That is, F1 hybrid individuals often consist of trats closely resemble one parent, intermediate, and resemble the other parent. Mosaicism indicates tr integration of parents are often collapse in F1 hybrids. Here we investigate the strength of trait mosaicism in male mating traits.
To quantify trait mosaicism in each crossing, we calculated dmismatch following Thompson et al. (2021). dmismatch was calculated as follows:
\(d_{mismatch} = \sqrt{2} * \sqrt{ Z_i^2 + Z_j^2 - \frac{Z_i + Z_j}{\sqrt{2}}^2 }\)
Where \(Z_i\), standardized hybrid trait value in trait \(i\), is
\(Z_i = \frac{hyb_i - spSS_i}{spLL_i - spSS_i}\)
\(Z_i\) is 0-1 if F1 phenotypic mean lies within the range of parents. We note, however, novelty in phenotypic means (transgressive segregation) can result in quite large \(Z_i\): when trait size is large and parental phenotypic divergence is small, novel hybrid phenotype will result in quite large \(Z_i\). This seems occurred in mice (see bellow)
dat <- original.dat %>%
select(Study.ID, Cross.ID, taxa, Genus, Reciprocal, contains("Mn")) %>%
# Gather reciprocal hybrid data
gather(
key = Hybrid, value = Mn.hybrid,
-c(Study.ID, Cross.ID, taxa, Genus, Reciprocal), -contains("Mn.sp")
) %>%
drop_na(Mn.hybrid) %>%
# Calculate standardized hybrid phenotype mean
mutate(
Dominance = ifelse(
Mn.sp1 > Mn.sp2,
(Mn.hybrid - Mn.sp2)/abs(Mn.sp1 - Mn.sp2),
(Mn.hybrid - Mn.sp1)/abs(Mn.sp1 - Mn.sp2)
)
) %>%
mutate_at("Cross.ID", as.factor) %>%
arrange(taxa, Genus, Cross.ID, Hybrid) %>%
# only get crosses with > 2 traits
# Reciprocal hybrids are separated
group_by(Cross.ID, Hybrid, taxa, Genus, Study.ID, Reciprocal) %>%
filter(n() > 2) %>%
# Create all combinations of standardized hybrid phenotype mean
do(data.frame(t(combn(.$Dominance, 2)))) %>%
# Calculate dominance mismatch
mutate(
Mismatch = sqrt(2) * sqrt( X1^2 + X2^2 - ( (X1 + X2) / sqrt(2) )^2 ),
Mismatch.log = log(Mismatch)
) %>%
left_join(meta) %>%
mutate_if(is.character, as.factor) %>%
within(taxa <- ordered(taxa, levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes"))) %>%
as.data.frame
dat %>%
summarise(
'Trait combination' = length(X1),
'Species pair' = length(unique(species.pair)),
'Crossing' = length(unique(Cross.ID))
) %>%
kable("html", caption = "Dataset summary") %>%
kable_styling("striped", position = "left")
| Trait combination | Species pair | Crossing |
|---|---|---|
| 15107 | 20 | 27 |
ggplot(data = dat, aes(x = Cross.ID, y = Mismatch)) +
geom_point(alpha = 0.4, aes(color = Hybrid)) +
xlab("Cross ID") + ylab("dmismatch") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(~taxa, scales = "free") +
ggtitle("All dmismatch values across orders")
ggplot(
data = dat %>%
filter(!grepl("musculus", Cross.ID)),
aes(x = Cross.ID, y = Mismatch)) +
xlab("Cross ID") + ylab("dmismatch") +
geom_point(alpha = 0.4, aes(color = Hybrid)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(~taxa, scales = "free") +
ggtitle("dmismatch across orders excluding Rodentia")
dmismatch is quite large in crossing involving mices (Rodentia), due to large trait value.
Estimated mean dmismatch across all studies by phylogenetic random model (using MCMCglmm function).
The model included following Random effects estimates:
Study.ID: Primary studies
sp1.name: Phylogeny of parental species
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations
Hybrid: Crossing direction
SE.units: Sampling variance of effect size
#++++++++++++++++++++++++++++++++++#
print("Phylogenetic tree for mcmcglmm")
[1] "Phylogenetic tree for mcmcglmm"
#++++++++++++++++++++++++++++++++++#
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.novelty.tre") %>%
compute.brlen(method = "Grafen", power = 1)
plot.phylo(phylo_branch, cex = 0.7)
# # saving phylogeneic matrix
# phylo_cor <- vcv(phylo_branch, cor = T)
#
# # generating inverse phylogenetic matrix for MCMCglmm
# phylo_branch$node.label <- NULL
# phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv
# phylmix <-MCMCglmm(
# fixed = Mismatch ~ 1,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + sp1.name + Cross.ID + Hybrid,
# verbose = FALSE,
# ginverse = list(sp1.name = phylo_MCMC),
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000, # Increase the number of burnin, default is 3000
# data = dat
# )
# saveRDS(phylmix, file = "../Analysis/mismatch.obj")
phylmix <- readRDS("../Analysis/mismatch.obj")
#+++++++++++++++++++++++++++#
# Fixed effects output
#+++++++++++++++++++++++++++#
get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
dplyr::select("Factors", "Estimates", "95% credible interval", "P", "Description") %>%
kable("html", digits = 3, caption = "Fixed effects estimates") %>%
kable_styling("striped", position = "left")
| Factors | Estimates | 95% credible interval | P | Description |
|---|---|---|---|---|
| (Intercept) | 4.41 | -3.05 – 10.29 | 0.166 | \(\beta\) = 4.41, CI = -3.05 – 10.29, P = 0.166 |
#+++++++++++++++++++++++++++#
# Random effects output
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV)) %>%
kable("html", digits = 2, caption = "Random effects estimates") %>%
kable_styling("striped", position = "left")
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 1.18 | 0–11.21 |
| sp1.name | 8.31 | 0–70.99 |
| Cross.ID | 42.58 | 13.07–101.37 |
| Hybrid | 28.14 | 0–61.22 |
| units | 2801.56 | 2740.38–2865.59 |
Even data included quite large dmismatch, overall it did not differ from 0. Yet, the estimate (β = 4.41, CI = -3.05 – 10.29) is still much larger than previously reported mean dmismatch 0.6 Thompson et al. 2019. This is presumably because novelty in phenotypic means, which should increase dmismatch, is much more frequent in our data compared to Thompson et al. 2019.
We asked if any species-level moderators (Genetic divergence between parents, Genet.divergence; Viability of reciprocal cross, Reciprocal; Heterogametic sex, Hetero.sex; Parents’ distribution overlap, Distribution) influence dmismatch values by phylogenetic random model. Random effects were identical as [above] (## General pattern of mismatch dmismatch).
dat3 <- dat %>%
drop_na(Genet.divergence, Reciprocal, Hetero.sex, Distribution)
# phylmix <-MCMCglmm(
# fixed = Mismatch ~ Genet.divergence + Reciprocal + Hetero.sex + Distribution,
# # idh(SE):units | weight by SE of effect size
# random = ~ Study.ID + sp1.name + Cross.ID + Hybrid,
# verbose = FALSE,
# ginverse = list(sp1.name = phylo_MCMC),
# nitt = 60000, # Increase the number of iterations, default is 13000
# burnin = 5000, # Increase the number of burnin, default is 3000
# data = dat3
# )
# saveRDS(phylmix, file = "../Analysis/mismatch.reg.obj")
phylmix <- readRDS("../Analysis/mismatch.reg.obj")
#+++++++++++++++++++++++++++#
# Fixed effects output
#+++++++++++++++++++++++++++#
get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
dplyr::select("Factors", "Estimates", "95% credible interval", "P", "Description") %>%
kable("html", digits = 3, caption = "Fixed effects estimates") %>%
kable_styling("striped", position = "left")
| Factors | Estimates | 95% credible interval | P | Description |
|---|---|---|---|---|
| (Intercept) | -1.72 | -27.5 – 23.1 | 0.903 | \(\beta\) = -1.72, CI = -27.5 – 23.1, P = 0.903 |
| Genet.divergence | -1.38 | -11.56 – 8.6 | 0.788 | \(\beta\) = -1.38, CI = -11.56 – 8.6, P = 0.788 |
| ReciprocalViable | -8.27 | -39.1 – 19.25 | 0.562 | \(\beta\) = -8.27, CI = -39.1 – 19.25, P = 0.562 |
| Hetero.sexMale | 11.74 | -24.15 – 50.92 | 0.512 | \(\beta\) = 11.74, CI = -24.15 – 50.92, P = 0.512 |
| DistributionAllopatry | -2.59 | -31.14 – 27.31 | 0.847 | \(\beta\) = -2.59, CI = -31.14 – 27.31, P = 0.847 |
#+++++++++++++++++++++++++++#
# Random effects output
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV)) %>%
kable("html", digits = 2, caption = "Random effects estimates") %>%
kable_styling("striped", position = "left")
| Random effects | Estimates | 95% credible interval |
|---|---|---|
| Study.ID | 2.54 | 0–23.35 |
| sp1.name | 4.78 | 0–48.3 |
| Cross.ID | 60.43 | 12.36–204.15 |
| Hybrid | 259.92 | 0–114.81 |
| units | 2874.72 | 2809.1–2941.75 |
None of species level moderators did not influence dmismatch, as Thompson et al. 2019 showed.
To investigate paternal effect in hybrids using difference in variance between reciprocal cross, it’s better to allign species with larger phenotypic variance (spLLV2) and smaller CV (spSSV2) in each traits. Name of hybrids will be changed according to change in names of parental species (hybridLSV2 & hybridSLV2).
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 >= sp2 in phenotypic variation
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp1sp2.var <- original.dat %>%
filter(SD.sp1 >= SD.sp2) # filtering according to CV of parentals
# change column name from sp1 or sp2 -> spL or spS
names(sp1sp2.var) <- gsub("1", "L", names(sp1sp2.var))
names(sp1sp2.var) <- gsub("2", "S", names(sp1sp2.var))
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 < sp2 in phenotypic variation
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp2sp1.var <- original.dat %>%
filter(SD.sp1 < SD.sp2)
# change column name from sp1 or sp2 -> spS or spL
names(sp2sp1.var) <- gsub("1", "S", names(sp2sp1.var))
names(sp2sp1.var) <- gsub("2", "L", names(sp2sp1.var))
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# combine dataset & sort by ES.ID
#+++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(sp1sp2.var, sp2sp1.var) %>%
arrange(ES.ID) %>%
write.csv("../data/dat.var.csv", quote=F,row.names = F)
We quantified relative phenotypic variability of hybrids and the parent with large variability (spLLV2) to the parent with small variability (spSSV2), by using lnVR. We compared variance itself, and did not controll mean-variance relationship.
Effecct size was calculated by using escalc function in metafor package
# load spLS files
dat <- read.csv("../data/dat.var.csv", head = TRUE)
# Order by taxon
dat$taxa <- ordered(
dat$taxa,
levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
)
var.dif <- foreach(
cr = c("hybLS", "hybSL", "spL"),
.combine = `rbind`
) %do% {
escalc(
measure = "VR",
# N of hybrids
n1i = dat[, paste("N", cr, sep = ".")],
# N of parentals
n2i = dat[, "N.spS"],
# Mean of hybrids
m1i = dat[, paste("Mn", cr, sep = ".")],
# Mean of parentals
m2i = dat[, "Mn.spS"],
# SD of hybrids]
sd1i = dat[, paste("SD", cr, sep = ".")],
# SD of parentals
sd2i = dat[, "SD.spS"],
data = dat
) %>%
rename(lnVR.es = yi, lnVR.sv = vi) %>%
mutate(cross = cr)
} %>%
drop_na(lnVR.es) %>%
arrange(ES.ID) %>%
left_join(., dat)
write.csv(var.dif, "../data/lnVR.ES.general.csv", row.names = F)
res <- rma(yi = lnVR.es, vi = lnVR.sv, data = var.dif, method="FE")
## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-5, 5))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-5, 5))
# load spLS files
dat <- read.csv("../data/dat.var.csv", head = TRUE)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between larger species (spL) and hybrids
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("spL")) { # identify type of hybrids
for (cr in c("hybLS", "hybSL")) { # identify type of parentals
assign(
paste(cr, i, sep = "_"),
escalc(
measure = "VR",
# N of hybrids
n1i = dat[, paste("N", i, sep = ".")],
# N of parentals
n2i = dat[, paste("N", cr, sep = ".")],
# Mean of hybrids
m1i = dat[, paste("Mn", i, sep = ".")],
# Mean of parentals
m2i = dat[, paste("Mn", cr, sep = ".")],
# SD of hybrids
sd1i = dat[, paste("SD", i, sep = ".")],
# SD of parentals
sd2i = dat[, paste("SD", cr, sep = ".")],
data = dat
) %>%
select(ES.ID, yi, vi) %>%
rename("lnVR.es" = "yi", "lnCVR.sv" = "vi") %>%
mutate(
# direction of transgressve segregation - greater than parent
direction = "+",
# SE for logistic distribution, for the weighted binomial regression
# SE = pi/sqrt(3*(n_e + n_c))
SE = pi/sqrt(3*(
dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
))
)
)
}
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between hybrids and smaller species (spS)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("hybLS", "hybSL")) { # identify type of hybrids
for (cr in c("spS")) { # identify type of parentals
assign(
paste(i, cr, sep = "_"),
escalc(
measure = "VR",
# N of hybrids
n1i = dat[, paste("N", i, sep = ".")],
# N of parentals
n2i = dat[, paste("N", cr, sep = ".")],
# Mean of hybrids
m1i = dat[, paste("Mn", i, sep = ".")],
# Mean of parentals
m2i = dat[, paste("Mn", cr, sep = ".")],
# SD of hybrids
sd1i = dat[, paste("SD", i, sep = ".")],
# SD of parentals
sd2i = dat[, paste("SD", cr, sep = ".")],
data = dat
) %>%
select(ES.ID, yi, vi) %>%
rename("lnVR.es" = "yi", "lnCVR.sv" = "vi") %>%
mutate(
# direction of transgressve segregation - smaller than parent
direction = "-",
# SE for logistic distribution, for the weighted binomial regression
# SE = pi/sqrt(3*(n_e + n_c))
SE = pi/sqrt(3*(
dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
))
)
)
}
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Data output
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(
## Difference with mother species ###
bind_rows(hybLS_spL, hybSL_spS) %>%
mutate(parental = "Mother"),
## Difference with father species ###
bind_rows(hybLS_spS, hybSL_spL) %>%
mutate(parental = "Father")
) %>%
drop_na(lnVR.es) %>%
left_join(., dat) %>%
# Assign novel variability to 1, non-novel variability to 0 for each effect size
mutate(Novelty = ifelse(lnVR.es < 0, 1, 0)) %>%
## Join with metadata ###
left_join(
.,
read.xlsx(
"../data/original.data.xlsx", sheet = "Species.level.moderators"
)
) %>%
mutate_at(vars(contains("divergence")), as.numeric) %>%
mutate_at(vars(contains("divergence")), scale) %>%
as.data.frame %>%
select(-trait) %>%
drop_na(Hetero.sex, trait.type, Genet.divergence) %>%
# order taxon
within(
taxa <- ordered(
taxa,
levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
)
) %>%
mutate_if(is.character, as.factor) %>%
# Insect or no
mutate(
insect = ifelse(taxa %in% c("Aves", "Rodentia", "Anura", "Cichliformes"), "no", "insect"),
) %>%
write.csv("../data/lnVR.ES.Novelty.csv", row.names = F)
# Load effect sizes
var.dif <- read.csv("../data/lnVR.ES.general.csv", head = TRUE)%>%
# indicate dataset including novel variability
mutate(data.type = "Alltraits")
# Observations with novel variability
All <- read.csv("../data/lnVR.ES.novelty.csv", head = TRUE) %>%
filter(lnVR.es < 0) %>%
distinct(ES.ID)
# Effect sizes with non-novel variability
Nonovel <- read.csv("../data/lnVR.ES.general.csv", head = TRUE) %>%
filter(!ES.ID %in% unique(All$ES.ID)) %>%
# indicate dataset without novel variability
mutate(data.type = "Nonnoveltraits")
# combine dataset with/without novel variability
Novel.Nonnovel <- bind_rows(var.dif, Nonovel) %>%
mutate(metaunit = str_c(data.type, cross, sep = "_")) %>%
# Use observations with both reciprocal crosses
drop_na(contains("Mn"), contains("SD")) %>%
# Insect or no
mutate(
insect = ifelse(taxa %in% c("Cichliformes", "Anura", "Aves", "Rodentia"), "no", "insect"),
)
#+++++++++++++++++++++++++++++++#
# Phylogeny
#+++++++++++++++++++++++++++++++#
# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
# names = levels(var.dif$spL.name) %>%
# str_replace_all("_", " "),
# context_name = "Animals"
# )
#
# # Create tree
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
# strip_ott_ids # remove OTT IDs from tip labels
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
#
# # Fix names of tip labels
# bin.tree$tip.label %<>% str_replace_all("Dryophytes", "Hyla")
# # Indicate mismatch between tip labels & dataset species names
# setdiff(levels(as.factor(bin.tree$tip.label)), levels(var.dif$spL.name))
# setdiff(levels(var.dif$spL.name), levels(as.factor(bin.tree$tip.label)))
#
# write.tree(bin.tree, file= "../data/phylo.lnVR.tre")
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.lnVR.tre") %>%
compute.brlen(bin.tree, method = "Grafen", power = 1)
# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)
# Note one of the tips is called "Lates calcarifer (estimated)"
phylo_branch$node.label <- NULL
# Plot tree
plot.phylo(phylo_branch, cex = 0.7)
We compared midparent-value of phenotypic variability and hybrids’ phenotypic variability by calculating log ratio of phenotypic variability (lnVR) of midparent-value and hybrids to spSSV.
It’s sampling variance was substitute by that of spLLV
We statistically compared those effect sizes through the formal meta-regression by using rma.mv function of R package metafor
All meta-analytic models included following Random effects estimates:
Study.ID: Primary studies. Denoted as Study
Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations. Denoted as crossed strain
spL.name: Phylogeny of parental species (spLLM). Denoted as species with phylogeny
#+++++++++++++++++++++++++++++++++++++++++++#
# Calculate midparent
#+++++++++++++++++++++++++++++++++++++++++++#
midparent <- Novel.Nonnovel %>%
mutate(
lnVR.midparent.es =
ifelse(
cross == "spL",
# log(Se/Sc)
log((SD.spL + SD.spS)/2) - log(SD.spS) +
# 1/2(Ne-1) - 1/2(Nc-1)
1/(N.spL + N.spS -2) - 1/(2*N.spS - 2),
lnVR.es
)
# Divide variance by 4 in spL whereas hybrids are constant
) %>%
mutate_at("cross", as.factor) %>%
within(levels(cross) <- c("hybLS", "hybSL", "midparent"))
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
# Compare midparent and hybrids by meta-regresson
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
for (tr in c("Alltraits", "Nonnoveltraits")) {
for (taxon in c("alltaxon", "insect")) {
# Filtering taxon (all taxon or insects)
if (taxon == "alltaxon") {
dat <- midparent %>%
filter(data.type == tr)
} else {
dat <- midparent %>%
filter(data.type == tr, insect == taxon)
}
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Estimate meta-analytic mean of midparent of phenotypic variation
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# midparent.compare <- rma.mv(
# yi = lnVR.midparent.es, V = lnVR.sv,
# data = dat,
# method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ relevel(cross, ref = "midparent")
# )
# ## Save model ###
# saveRDS(
# midparent.compare,
# file = paste("../Analysis/lnVR.midparent.compare", tr, taxon, "obj", sep = ".")
# )
assign(
paste("Dominance", tr, taxon, sep = "."),
readRDS(paste("../Analysis/lnVR.midparent.compare", tr, taxon, "obj", sep = ".")) %>%
get_reg() %>%
# Show dataset name
within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
# Rename fixed effects
within('Fixed effects' <- c("", "midparent (intrcpt)", "hybLS", "hybSL")) %>%
# Difference of each cross from midparent in %
mutate('Comparison with midparent' = c(
rep("", 2),
# Difference hybLS - midparent
paste(
round(
100*(exp(midparent.compare$beta[1]+midparent.compare$beta[2]) -exp(midparent.compare$beta[1])), # hybrid LS
2),
"% larger"
),
# beta[1] : midparent
# beta[2] : hybLS
# beta[3] : hybSL
# Difference hybSL - midparent
paste(
round(
100*(exp(midparent.compare$beta[1]+midparent.compare$beta[3])-exp(midparent.compare$beta[1])), # hybrid SL
2),
"% larger"
)
))
)
#++++++++++++++++++++++++++++++++++++++++++++++#
# Meta-analysis for midparent to plot band
#++++++++++++++++++++++++++++++++++++++++++++++#
# midparent.random <- rma.mv(
# yi = lnVR.midparent.es,
# V = lnVR.sv,
# data = dat %>%
# filter(cross == "midparent"),
# method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor)
# )
# ## Save model ###
# saveRDS(
# midparent.random,
# file = paste("../Analysis/lnVR.midparent", tr, taxon, "obj", sep = ".")
# )
}
}
bind_rows(
Dominance.Alltraits.alltaxon, Dominance.Nonnoveltraits.alltaxon,
Dominance.Alltraits.insect, Dominance.Nonnoveltraits.insect
) %>%
kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | Comparison with midparent |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | 87.9% | 1.2% | 14.2% | 0% | 72.6% | |||||||
| midparent (intrcpt) | 0.542 | 0.340 | 0.743 | 0.000 |
|
|||||||
| hybLS | -0.049 | -0.116 | 0.019 | 0.157 | -21.11 % larger | |||||||
| hybSL | -0.083 | -0.150 | -0.016 | 0.015 |
|
-24.94 % larger | ||||||
| Nonnoveltraits alltaxon | 82.1% | 0% | 23.9% | 0% | 58.2% | |||||||
| midparent (intrcpt) | 0.756 | 0.536 | 0.976 | 0.000 |
|
|||||||
| hybLS | -0.142 | -0.232 | -0.052 | 0.002 |
|
-21.11 % larger | ||||||
| hybSL | -0.140 | -0.228 | -0.051 | 0.002 |
|
-24.94 % larger | ||||||
| Alltraits insect | 87.7% | 0% | 10% | 0% | 77.7% | |||||||
| midparent (intrcpt) | 0.478 | 0.300 | 0.656 | 0.000 |
|
|||||||
| hybLS | -0.063 | -0.133 | 0.006 | 0.074 | -21.11 % larger | |||||||
| hybSL | -0.107 | -0.176 | -0.038 | 0.002 |
|
-24.94 % larger | ||||||
| Nonnoveltraits insect | 82.4% | 0% | 24.2% | 0% | 58.2% | |||||||
| midparent (intrcpt) | 0.722 | 0.485 | 0.960 | 0.000 |
|
|||||||
| hybLS | -0.167 | -0.260 | -0.074 | 0.000 |
|
-21.11 % larger | ||||||
| hybSL | -0.179 | -0.270 | -0.088 | 0.000 |
|
-24.94 % larger |
We statistically compared lnVR from spSSM to the other crosses through the formal meta-regression by using rma.mv function of R package metafor.
Here we asked if lnVR of hybSLV was smaller/larger than that of hybLSV. Smaller lnVR of hybSLV indicates maternal inheritance in phenotypic variability
for (tr in c("Alltraits", "Nonnoveltraits")) {
if (taxon == "alltaxon") {
dat <- Novel.Nonnovel %>%
filter(data.type == tr)
} else {
dat <- Novel.Nonnovel %>%
filter(data.type == tr, insect == taxon)
}
for (taxon in c("alltaxon", "insect")) {
#++++++++++++++++++++++++++++++++++++++++++++++#
# phylogenetic random regresson (ANOVA)
#++++++++++++++++++++++++++++++++++++++++++++++#
# phyl.random.hybLS <- rma.mv(
# yi = lnVR.es, V = lnVR.sv,
# data = dat, method = "REML",
# random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
# R = list(spL.name = phylo_cor),
# mods = ~ cross
# )
# # Save model ###
# saveRDS(
# phyl.random.hybLS,
# file = paste("../Analysis/lnVR.compare.from.hybLS", tr, taxon, "obj", sep = ".")
# )
phyl.random.hybLS <- readRDS(
paste("../Analysis/lnVR.compare.from.hybLS", tr, taxon, "obj", sep = ".")
)
assign(
paste("Reciprocal", tr, taxon, sep = "."),
phyl.random.hybLS %>%
get_reg() %>%
# Show dataset name
within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
# Rename fixed effects
within('Fixed effects' <- c("", "hybLS (intrcpt)", "hybSL", "spLL")) %>%
# Difference of each cross from midparent in %
mutate('Comparison with hybridLS' = c(
rep("", 2),
paste(
round(
(exp(phyl.random.hybLS$beta[1]+phyl.random.hybLS$beta[2]) -
exp(phyl.random.hybLS$beta[1])
)*100,
2),
"% larger"
),
""
)
)
)
}
}
bind_rows(
Reciprocal.Alltraits.alltaxon, Reciprocal.Nonnoveltraits.alltaxon,
Reciprocal.Alltraits.insect, Reciprocal.Nonnoveltraits.insect
) %>%
kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")
| Dataset | Fixed effects | Estimate | LowerCI | UpperCI | P | I2[total] | %I2[species with phylogeny] | %I2[study] | %I2[crossed strain] | %I2[residual] | significance | Comparison with hybridLS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alltraits alltaxon | 89.6% | 0% | 14.2% | 0% | 75.3% | |||||||
| hybLS (intrcpt) | 0.498 | 0.301 | 0.696 | 0.000 |
|
|||||||
| hybSL | -0.032 | -0.104 | 0.039 | 0.378 | -5.21 % larger | |||||||
| spLL | 0.280 | 0.212 | 0.347 | 0.000 |
|
|||||||
| Nonnoveltraits alltaxon | 85.8% | 0% | 23.4% | 0% | 62.4% | |||||||
| hybLS (intrcpt) | 0.641 | 0.392 | 0.890 | 0.000 |
|
|||||||
| hybSL | 0.007 | -0.088 | 0.101 | 0.893 | 1.24 % larger | |||||||
| spLL | 0.416 | 0.326 | 0.506 | 0.000 |
|
|||||||
| Alltraits insect | 89.4% | 0% | 9.6% | 0% | 79.8% | |||||||
| hybLS (intrcpt) | 0.425 | 0.233 | 0.618 | 0.000 |
|
|||||||
| hybSL | -0.042 | -0.116 | 0.032 | 0.261 | -6.35 % larger | |||||||
| spLL | 0.293 | 0.223 | 0.363 | 0.000 |
|
|||||||
| Nonnoveltraits insect | 86.1% | 0% | 23.6% | 0% | 62.5% | |||||||
| hybLS (intrcpt) | 0.582 | 0.312 | 0.852 | 0.000 |
|
|||||||
| hybSL | -0.009 | -0.107 | 0.089 | 0.857 | -1.6 % larger | |||||||
| spLL | 0.438 | 0.345 | 0.531 | 0.000 |
|
sessionInfo() # show R version etc
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=Japanese_Japan.932 LC_CTYPE=Japanese_Japan.932
[3] LC_MONETARY=Japanese_Japan.932 LC_NUMERIC=C
[5] LC_TIME=Japanese_Japan.932
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggimage_0.2.8 patchwork_1.1.1 ggtree_2.1.1 ggbeeswarm_0.6.0
[5] ggiraphExtra_0.3.0 svglite_1.2.3.2 pander_0.6.3 kableExtra_1.3.1
[9] knitr_1.30 phytools_0.7-70 maps_3.3.0 rotl_3.0.11
[13] orchaRd_0.0.0.9000 MCMCglmm_2.30 ape_5.4-1 coda_0.19-4
[17] metafor_2.4-0 Matrix_1.2-18 foreach_1.5.1 magrittr_2.0.1
[21] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[25] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
[29] tidyverse_1.3.0 openxlsx_4.2.3
loaded via a namespace (and not attached):
[1] readxl_1.3.1 uuid_0.1-4 backports_1.2.1
[4] fastmatch_1.1-0 systemfonts_0.3.2 plyr_1.8.6
[7] igraph_1.2.6 lazyeval_0.2.2 splines_3.6.3
[10] mycor_0.1.1 rncl_0.8.4 digest_0.6.27
[13] htmltools_0.5.0 magick_2.5.2 fansi_0.4.1
[16] modelr_0.1.8 prettyunits_1.1.1 colorspace_2.0-0
[19] rvest_0.3.6 haven_2.3.1 xfun_0.20
[22] crayon_1.3.4 jsonlite_1.7.2 phangorn_2.5.5
[25] iterators_1.0.13 glue_1.4.2 gtable_0.3.0
[28] ppcor_1.1 webshot_0.5.2 sjmisc_2.8.6
[31] rentrez_1.2.3 scales_1.1.1 DBI_1.1.0
[34] Rcpp_1.0.5 plotrix_3.7-8 viridisLite_0.3.0
[37] progress_1.2.2 tmvnsim_1.0-2 gridGraphics_0.5-1
[40] tidytree_0.3.3 htmlwidgets_1.5.3 httr_1.4.2
[43] RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.0.3
[46] pkgconfig_2.0.3 XML_3.99-0.3 dbplyr_2.0.0
[49] labeling_0.4.2 ggplotify_0.0.5 tidyselect_1.1.0
[52] rlang_0.4.10 reshape2_1.4.4 munsell_0.5.0
[55] cellranger_1.1.0 tools_3.6.3 cli_2.2.0
[58] generics_0.1.0 pacman_0.5.1 sjlabelled_1.1.7
[61] broom_0.7.3 evaluate_0.14 yaml_2.2.1
[64] fs_1.5.0 zip_2.1.1 nlme_3.1-151
[67] ggiraph_0.7.8 xml2_1.3.2 compiler_3.6.3
[70] rstudioapi_0.13 beeswarm_0.2.3 curl_4.3
[73] reprex_0.3.0 treeio_1.11.2 clusterGeneration_1.3.7
[76] stringi_1.5.3 highr_0.8 gdtools_0.2.3
[79] cubature_2.0.4.1 lattice_0.20-38 tensorA_0.36.2
[82] vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0
[85] BiocManager_1.30.10 combinat_0.0-8 insight_0.11.1
[88] corpcor_1.6.9 R6_2.5.0 vipor_0.4.5
[91] codetools_0.2-16 MASS_7.3-53 gtools_3.8.2
[94] assertthat_0.2.1 withr_2.3.0 mnormt_2.0.2
[97] mgcv_1.8-31 expm_0.999-5 parallel_3.6.3
[100] hms_0.5.3 quadprog_1.5-8 grid_3.6.3
[103] rmarkdown_2.6 rvcheck_0.1.8 numDeriv_2016.8-1.1
[106] scatterplot3d_0.3-41 lubridate_1.7.9.2